From 5c9c57e4a660016dd6ce00d0d1d7e1ea01f1965c Mon Sep 17 00:00:00 2001
From: Wines <daniel.wines@nist.gov>
Date: Tue, 3 Dec 2024 12:53:39 -0500
Subject: [PATCH] Adding updated chipsff benchmarks

---
 jarvis_leaderboard/benchmarks/.DS_Store       |  Bin 6148 -> 0 bytes
 jarvis_leaderboard/benchmarks/AI/.DS_Store    |  Bin 14340 -> 0 bytes
 .../benchmarks/AI/MLFF/.DS_Store              |  Bin 14340 -> 0 bytes
 .../AI/SinglePropertyPrediction/.DS_Store     |  Bin 16388 -> 0 bytes
 .../dft_3d_chipsff_a.json.zip                 |  Bin 698 -> 1458 bytes
 .../dft_3d_chipsff_b.json.zip                 |  Bin 695 -> 1455 bytes
 .../dft_3d_chipsff_c.json.zip                 |  Bin 679 -> 1438 bytes
 .../dft_3d_chipsff_c11.json.zip               |  Bin 604 -> 1299 bytes
 .../dft_3d_chipsff_c44.json.zip               |  Bin 596 -> 1273 bytes
 .../dft_3d_chipsff_form_en.json.zip           |  Bin 610 -> 1381 bytes
 .../dft_3d_chipsff_kv.json.zip                |  Bin 642 -> 1336 bytes
 .../dft_3d_chipsff_surf_en.json.zip           |  Bin 1942 -> 1965 bytes
 .../dft_3d_chipsff_vac_en.json.zip            |  Bin 0 -> 1142 bytes
 .../dft_3d_chipsff_vol.json.zip               |  Bin 1104 -> 2082 bytes
 .../ES/SinglePropertyPrediction/.DS_Store     |  Bin 6148 -> 0 bytes
 .../alignn_ff-5.27.2024/.DS_Store             |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 704 -> 0 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 705 -> 0 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 688 -> 0 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1049 -> 0 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1047 -> 0 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1080 -> 0 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1047 -> 0 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1802 -> 0 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1036 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 970 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 966 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 948 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1462 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1484 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1484 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1456 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1201 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 754 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1451 bytes
 .../metadata.json                             |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1002 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 996 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 978 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1550 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1559 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1590 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1559 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1393 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 784 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 1538 bytes
 .../metadata.json                             |    0
 .../{orb-v1 => alignn_ff_5_27_24}/run.sh      |    0
 .../run_chipsff.py                            |    0
 .../alignn_hydride_supercon/.DS_Store         |  Bin 6148 -> 0 bytes
 .../.DS_Store                                 |  Bin 6148 -> 0 bytes
 .../contributions/chgnet/.DS_Store            |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 1014 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 733 -> 1021 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 702 -> 1004 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1090 -> 1623 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1103 -> 1624 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1143 -> 1657 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1096 -> 1627 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1873 -> 1515 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 835 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1098 -> 1620 bytes
 .../crystal_feature_model/parity_plot.png     |  Bin 23716 -> 0 bytes
 .../contributions/eqV2_153M_omat/.DS_Store    |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 730 -> 1022 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 1023 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 708 -> 1001 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1114 -> 1604 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1107 -> 1591 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1150 -> 1632 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1106 -> 1601 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1794 -> 1392 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 781 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1099 -> 1589 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 726 -> 1016 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 727 -> 1013 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 704 -> 998 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1109 -> 1625 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1095 -> 1627 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1132 -> 1644 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1090 -> 1632 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1727 -> 1397 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 809 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1095 -> 1617 bytes
 .../eqV2_31M_omat_mp_salex/.DS_Store          |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 1019 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 1016 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 708 -> 1002 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1109 -> 1616 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1121 -> 1629 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1150 -> 1661 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1106 -> 1620 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1907 -> 1485 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 853 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1106 -> 1624 bytes
 .../contributions/eqV2_86M_omat/.DS_Store     |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 722 -> 1020 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 1018 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 711 -> 1005 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1104 -> 1615 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1111 -> 1624 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1134 -> 1653 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1107 -> 1610 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1754 -> 1351 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 792 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1107 -> 1618 bytes
 .../eqV2_86M_omat_mp_salex/.DS_Store          |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 733 -> 1025 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 731 -> 1011 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 706 -> 1011 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1098 -> 1626 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1115 -> 1632 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1148 -> 1661 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1112 -> 1622 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1919 -> 1498 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 836 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1096 -> 1620 bytes
 .../contributions/mace-alexandria/.DS_Store   |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 730 -> 1010 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 731 -> 1016 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 708 -> 1009 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1111 -> 1598 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1117 -> 1617 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1134 -> 1636 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1096 -> 1583 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1773 -> 1364 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 764 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1097 -> 1586 bytes
 .../contributions/mace/.DS_Store              |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 726 -> 1019 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 724 -> 1020 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 713 -> 1006 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1109 -> 1606 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1114 -> 1629 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1157 -> 1662 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1112 -> 1611 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1876 -> 1459 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 776 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1109 -> 1619 bytes
 .../contributions/matgl-direct/.DS_Store      |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 721 -> 1009 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 727 -> 1014 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 709 -> 996 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1112 -> 1581 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1111 -> 1592 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1143 -> 1618 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1108 -> 1558 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1869 -> 1469 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 819 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1100 -> 1569 bytes
 .../contributions/matgl/.DS_Store             |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 723 -> 1020 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 727 -> 1026 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 708 -> 1014 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1110 -> 1615 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1106 -> 1644 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1148 -> 1664 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1111 -> 1625 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1810 -> 1496 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 811 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1100 -> 1604 bytes
 .../mlearn_analysis_Cu_alignn_ff/.DS_Store    |  Bin 6148 -> 0 bytes
 ...-forces-mlearnall_Cu-test-multimae.csv.zip |  Bin 0 -> 752158 bytes
 .../metadata.json                             |    0
 .../mlearn_alignn_ff_12_2_24_job_info.json    |    8 +
 .../mlearn_analysis_log.txt                   |   39 +
 .../run.sh                                    |  148 ++
 .../run_chipsff.py                            | 2286 +++++++++++++++++
 ...-forces-mlearnall_Cu-test-multimae.csv.zip |  Bin
 .../metadata.json                             |    0
 .../mlearn_alignn_ff_job_info.json            |    0
 .../mlearn_analysis_log.txt                   |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 .../mlearn_analysis_Ge_alignn_ff/.DS_Store    |  Bin 6148 -> 0 bytes
 ...-forces-mlearnall_Ge-test-multimae.csv.zip |  Bin 0 -> 395833 bytes
 .../metadata.json                             |    0
 .../mlearn_alignn_ff_12_2_24_job_info.json    |    8 +
 .../mlearn_analysis_log.txt                   |   35 +
 .../run.sh                                    |  148 ++
 .../run_chipsff.py                            | 2286 +++++++++++++++++
 ...-forces-mlearnall_Ge-test-multimae.csv.zip |  Bin
 .../metadata.json                             |    0
 .../mlearn_alignn_ff_job_info.json            |    0
 .../mlearn_analysis_log.txt                   |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 .../mlearn_analysis_Li_alignn_ff/.DS_Store    |  Bin 6148 -> 0 bytes
 ...-forces-mlearnall_Li-test-multimae.csv.zip |  Bin 0 -> 369589 bytes
 .../metadata.json                             |    0
 .../mlearn_alignn_ff_12_2_24_job_info.json    |    8 +
 .../mlearn_analysis_log.txt                   |   36 +
 .../run.sh                                    |  148 ++
 .../run_chipsff.py                            | 2286 +++++++++++++++++
 ...-forces-mlearnall_Li-test-multimae.csv.zip |  Bin
 .../metadata.json                             |    0
 .../mlearn_alignn_ff_job_info.json            |    0
 .../mlearn_analysis_log.txt                   |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 .../mlearn_analysis_Mo_alignn_ff/.DS_Store    |  Bin 6148 -> 0 bytes
 ...-forces-mlearnall_Mo-test-multimae.csv.zip |  Bin 0 -> 335763 bytes
 .../metadata.json                             |   15 +
 .../mlearn_alignn_ff_12_2_24_job_info.json    |    8 +
 .../mlearn_analysis_log.txt                   |   31 +
 .../run.sh                                    |  148 ++
 .../run_chipsff.py                            | 2286 +++++++++++++++++
 ...-forces-mlearnall_Mo-test-multimae.csv.zip |  Bin
 .../metadata.json                             |   15 +
 .../mlearn_alignn_ff_job_info.json            |    0
 .../mlearn_analysis_log.txt                   |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 .../mlearn_analysis_Ni_alignn_ff/.DS_Store    |  Bin 6148 -> 0 bytes
 ...-forces-mlearnall_Ni-test-multimae.csv.zip |  Bin 0 -> 775816 bytes
 .../metadata.json                             |   15 +
 .../mlearn_alignn_ff_12_2_24_job_info.json    |    8 +
 .../mlearn_analysis_log.txt                   |   39 +
 .../run.sh                                    |  148 ++
 .../run_chipsff.py                            | 2286 +++++++++++++++++
 ...-forces-mlearnall_Ni-test-multimae.csv.zip |  Bin
 .../metadata.json                             |   15 +
 .../mlearn_alignn_ff_job_info.json            |    0
 .../mlearn_analysis_log.txt                   |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 .../mlearn_analysis_Si_alignn_ff/.DS_Store    |  Bin 6148 -> 0 bytes
 ...-forces-mlearnall_Si-test-multimae.csv.zip |  Bin 0 -> 380512 bytes
 .../metadata.json                             |   15 +
 .../mlearn_alignn_ff_12_2_24_job_info.json    |    8 +
 .../mlearn_analysis_log.txt                   |   33 +
 .../run.sh                                    |  148 ++
 .../run_chipsff.py                            | 2286 +++++++++++++++++
 ...-forces-mlearnall_Si-test-multimae.csv.zip |  Bin
 .../metadata.json                             |   15 +
 .../mlearn_alignn_ff_job_info.json            |    0
 .../mlearn_analysis_log.txt                   |    0
 .../run.sh                                    |    0
 .../run_chipsff.py                            |    0
 .../contributions/orb-v1/.DS_Store            |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 0 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 729 -> 0 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 706 -> 0 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1107 -> 0 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1138 -> 0 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1152 -> 0 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1102 -> 0 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1955 -> 0 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1107 -> 0 bytes
 .../contributions/orb-v2/.DS_Store            |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 731 -> 1020 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 728 -> 1019 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 708 -> 1012 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1100 -> 1614 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1128 -> 1646 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1142 -> 1658 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1099 -> 1610 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1901 -> 1502 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 857 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1095 -> 1616 bytes
 .../contributions/qmcpack_dmc_pbe/.DS_Store   |  Bin 6148 -> 0 bytes
 .../contributions/sevennet/.DS_Store          |  Bin 6148 -> 0 bytes
 ...ediction-a-dft_3d_chipsff-test-mae.csv.zip |  Bin 722 -> 1025 bytes
 ...ediction-b-dft_3d_chipsff-test-mae.csv.zip |  Bin 727 -> 1022 bytes
 ...ediction-c-dft_3d_chipsff-test-mae.csv.zip |  Bin 707 -> 1005 bytes
 ...iction-c11-dft_3d_chipsff-test-mae.csv.zip |  Bin 1110 -> 1612 bytes
 ...iction-c44-dft_3d_chipsff-test-mae.csv.zip |  Bin 1108 -> 1624 bytes
 ...on-form_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1163 -> 1653 bytes
 ...diction-kv-dft_3d_chipsff-test-mae.csv.zip |  Bin 1103 -> 1615 bytes
 ...on-surf_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 1880 -> 1480 bytes
 ...ion-vac_en-dft_3d_chipsff-test-mae.csv.zip |  Bin 0 -> 738 bytes
 ...iction-vol-dft_3d_chipsff-test-mae.csv.zip |  Bin 1098 -> 1607 bytes
 274 files changed, 14955 insertions(+)
 delete mode 100644 jarvis_leaderboard/benchmarks/.DS_Store
 delete mode 100644 jarvis_leaderboard/benchmarks/AI/.DS_Store
 delete mode 100644 jarvis_leaderboard/benchmarks/AI/MLFF/.DS_Store
 delete mode 100644 jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/.DS_Store
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diff --git a/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_c.json.zip b/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_c.json.zip
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diff --git a/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_c11.json.zip b/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_c11.json.zip
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diff --git a/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_c44.json.zip b/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_c44.json.zip
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delta 551
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diff --git a/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_form_en.json.zip b/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_form_en.json.zip
index 99bbfa134c334247bf7d9a31592d5fa2db3df2a4..9f99b7ba6fa41609422c352758e48a8290c5f002 100644
GIT binary patch
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delta 556
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diff --git a/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_kv.json.zip b/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_kv.json.zip
index 435d2f522bdc0b5b691719aeb182278657f249cf..55ba7364393a8ec14196f79d2363834511f7cf8e 100644
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diff --git a/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_surf_en.json.zip b/jarvis_leaderboard/benchmarks/AI/SinglePropertyPrediction/dft_3d_chipsff_surf_en.json.zip
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diff --git a/jarvis_leaderboard/contributions/chgnet/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/chgnet/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index 1294cb2954991c6f1a446d20fef9b038974b79bf..f29b48b5b5bc4cdc2a0e1c333280f47b02cae32a 100644
GIT binary patch
delta 1488
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delta 963
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diff --git a/jarvis_leaderboard/contributions/chgnet/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/chgnet/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index fecdfc356a3ff9a7e18a12c800b26ed97a187325..8a18c38219a8263e7364400682ed2801a564e431 100644
GIT binary patch
delta 1518
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delta 1000
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diff --git a/jarvis_leaderboard/contributions/chgnet/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/chgnet/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index 87a89725f5f4a2eb055b17c5e810332e69ff9f4f..46d050d50e97206316584b6551d23594ed9d950a 100644
GIT binary patch
delta 1493
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vCk}+?s{aR2O928D0~7!N00;o1QiE9ufs_x01pojH43p#oUIyI-00000F;C{3

delta 958
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delta 596
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diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 800741c170264da9cdd8ab26d6c69d60bbba735c..53ad5e132d6ce266acc5c8e45fd069fb09c04e39 100644
GIT binary patch
delta 886
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delta 595
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diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index a8b1726c40fbc2475e52be3f108edf95d0ef7d8b..f4e901a0de1fba60afca5e084a38b06b20cbf911 100644
GIT binary patch
delta 864
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delta 569
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diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 0970c7a2698c3adbaeec7f674f0b0104253eaeb0..d41a22f9d48e7a9fd8c3dc8aee19c533fa4c97b0 100644
GIT binary patch
delta 1468
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delta 979
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B-EIH?

diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index 13a540206e2db28fab6f24b748ac403cfb4c5eb8..bbcf0d288cf8f96ec502e735ce746b644b16b9a5 100644
GIT binary patch
delta 1460
zcmV;l1xxzV2)7IyP)h>@6aWAK2mqr}gIP9;>X}~!0089+0029a{{udMO_sZ^Y_|=D
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OlkNjw2Dk+P0001DJ<zBC

delta 967
zcmV;&133J*4ATf0P)h>@6aWAK2mpdvc3F!D!by7r005u|kr+aMmCKH8H4H@Od`oWR
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z7mU~NHDxbrl2bBo&#f|i9uUTM8Wz{ha~sSH&RG0)NOXT2wvQDv!z#$$5?@hn8399l
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zDn3sT=^loV2Og5--0G8f9xFj6!9s$Ss=SWp@j`twN%D;$xC-yFr1v<!t$l7p1;?u2
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p4^T@10u%!j000080D@U|S&Ik4NqYkT0H6nx(gj}z)B^wj007fT(cu69

diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 7bda2c9aa8f11c25bd6654cccdba72163a521cea..1eb6363838a0d0d3c76942c6d76c49eeefca72d9 100644
GIT binary patch
delta 1488
zcmV;>1uy#k2;dADP)h>@6aWAK2mqr}gIPt&Fmiwe0065Dkr+mQmc5R4H4KD%u4VU?
z?Kn<c(o!KIaRYo5+W<n`zj&PNN#1IIe3ICn@r=KIeEjq8pFh6-`0wlQzkd4V_n&|J
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zo4o~3Jz<yqeorE49&`HeK5qI(b;1vtYJEzdI_5kVOxe(XMCX)DuMCKu=6MdOc;||c
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zvbbuNwzaA|a0bLjZw```3Q*f?WWof3q}`vqQ3l(mX-`%g;OEdwlSLvj@ggnEhebAS
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zW~p79^XzY~D+nc%KG%8{b{oH3n%ij$Z!6Rmled|4pk7}@F#Z@6#INx_TD!Ic22q2m
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q`X5kB0Rj{Q6aWAK2mqr}gIPt&Fmiwe0065DlNAJG2Hgb!00009G0e{Z

delta 1002
zcmV<G0~P$>4E_ihP)h>@6aWAK2mpdvc3Cv}H`AU2002M;kr+mQl|8N;M+}5>o#p9)
z&3}eY6&Wz#3s?@6*oNWz!(Y$r^a|KuciC)`#j5Hb-#`BO_s{P?{`l|5-+z7i_WLit
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z3${T$9uG!2j6(p|6QGNi4=&b};NSD+?2FSIJhU_2JTQ7#({O%s5?J}01B~c)D1E=4
zw9WOYxt+LHAHkTvRtJ+vEZc(gWB-%+dVIll%lPu*W6)^i4i_}UD5_vjXTDv~wu@3e
zo%c;Dc-~qL^m%Dn1)lP(_oZcdD%)I-5@h9FrmQ)7=`a5SP)h*<6ay3h000O8f?0N1
YH2F8vo&x{?KnRof1!D&L0{{R30L9t&J^%m!

diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index e81fd44b277222f3cd83e678df9b1b7ffb3c9b9d..32c5108b944d84d71cd79b0bcb0317418c16c3a5 100644
GIT binary patch
delta 1472
zcmV;x1wZ=I2*C^+P)h>@6aWAK2mqr}gIOH*W{YbD0080(0026Z{{uaLO_ocJWHk^)
z=e-tV+3}AdQ$|QgY=CCa6F`Xli{n(KU#irts&u~R_?&wqetaMQ{QKwkAAkJ!<L|$|
z{qp<Izy12t-b;tZl=hvgMY^}y*C8KjkMOA;OSe^uTiJayZDdTfP8Unh75YBR(imnj
zmxyoO%Sy%HPm0KJ*|Tzg+ZK%#vGskaMBC8bv%6Y)yYr59Z(U1sSjDVfTZn6g*~+iu
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agIOH*W{YbD0080(lkNjv2Ezpa0000wd&?34

delta 973
zcmV;;12X)<4AKZ2P)h>@6aWAK2mpdvc3G9(iPC%n005W=0026Z?gc%6O_jZlAVmy>
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diff --git a/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_153M_omat/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip
new file mode 100644
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delta 587
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 1c10a2fffe47ed2ce7f5896b17dc5a3daf96f13e..c8cd17c6b130b102c942c9b782d2c750eb74e2ea 100644
GIT binary patch
delta 876
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delta 588
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 4709fa9ba62bc8be10b7f0780cc99bb800e9e164..6d0ad2ea7fea01daefc84a3ff955e83fff5717bc 100644
GIT binary patch
delta 866
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delta 565
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index a68a8b0601bffab0601a8d034c91f1a285a7e91a..2b9f90fa92f55222ec34788cff9493a2a07e3afa 100644
GIT binary patch
delta 1494
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delta 969
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index d77bd72947f9a73e39c12f1b3a85f3b0cd70dcdc..00b20d7f45d65668b1f71f0375bfc32f0f9fab57 100644
GIT binary patch
delta 1491
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delta 955
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index e23031c72a25c580ed7c8b1bb0b2bcee794fc573..ac0d642c1d6caade1c5917440de75abbd7f361bf 100644
GIT binary patch
delta 1500
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C<K(>n

delta 984
zcmV;}11J3K4D1LPP)h>@6aWAK2mpdvc3Bj_U^I*a001lqkr+jPMOC?u95)cn^)0i5
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G0002y>%|!W

diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index e1f895965c37121ed3500aeecccb5a02dd98c8f1..e5de99304136b5ba1e1e1548ac331c285b020b68 100644
GIT binary patch
delta 1503
zcmV<51t9vu2;dAGP)h>@6aWAK2mqr}gIRg6e_M(L000jR0026Z?*l!5O_sZ^Ww#B6
zdq0aTh*y!NTUQ1G<OSjs(<FwG_eVbMIZNvRgDH;oqNtz$g+KlrfBpUMKY#q|e?R{F
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z4XasW4E#HlCH`!!tOUROA5cpH0u%!j000080HacaS$VI2TZ#n$01ph4-UD6+;spQz
F005p*;nDyA

delta 957
zcmV;u148`Z48jN-P)h>@6aWAK2mpdvc3ICHFP>}z004vs0026Z4F)}bMO8bFY&8(g
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f2mpc&S$0{^950@10{{So2b25-UIxSi00000QJck6

diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip
index ddc42c67b6c185cffc2b123fd3f54bd88e933392..7ce2e7e031be2c4df2378e491254b0bbac5b9d9b 100644
GIT binary patch
delta 1251
zcmV<91RVRn4fP5bP)h>@6aWAK2mqu~gIP4k`An4r004jxkr+mQSIKH*M-aTvw`iK&
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4a5afaf45d909052ae4906e21788f61e47e5a645
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/.DS_Store b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/.DS_Store
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-a-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-a-dft_3d_chipsff-test-mae.csv.zip
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delta 590
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index d1d093cccf26ac65a7a68599838ad217ad1bef8d..e63caf258afe458a216350bdfff0cc947d3196b1 100644
GIT binary patch
delta 879
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delta 590
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 746918ab942aa1459d0dcc1c90c8c834462e0b7a..cb784df0341c3b580f77f00daaf64190345b75c5 100644
GIT binary patch
delta 865
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delta 569
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 188b20a43f0efd5e520b2ef4fc4f732e782af50d..218e505c7cd862d4ab45fb5a57108f7da7a149b5 100644
GIT binary patch
delta 1485
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delta 974
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index ee4a84f55af1c022a87d4ef1b3dbe7d4a7e9df17..aee66c370d9272a3665797803c244948aefcdbf4 100644
GIT binary patch
delta 1498
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Au>b%7

delta 982
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E02E`!m;e9(

diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 3a8829b8f1eb3cfcca2304ff294fc281ec006dfa..2d7cfc1169a334335ae92cf32fcb3bffa45a3cf0 100644
GIT binary patch
delta 1518
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Uodo~@+YFNx1Y-vJ1poj50G<N_hyVZp

delta 1003
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index 6dfdc509f3fcb8b9ac044b9fa3525924c076727a..690c4938128010b32c2ce9c1ac8c00fc982076f6 100644
GIT binary patch
delta 1491
zcmV;^1uXi~2-FN4P)h>@6aWAK2mqr}gIPd*y3BqB0009F0026Z{{uaLO_s}!t+ft>
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delta 973
zcmV;;12X*74AKZ2P)h>@6aWAK2mpdvc3DFM3?_U7005W=0026Z0R}yPO_e)tUPlat
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diff --git a/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_31M_omat_mp_salex/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip
index c4a84be9973f1f6f09844218ef06653f15fad5f9..9dde50c46e0708b2b1a5df6436f1bce72683bc86 100644
GIT binary patch
delta 1340
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delta 583
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 482c822acb687f20de0a60b9fa44dde4673b2484..b6aa60114405e2a502401ad4a043cecb2d876eb8 100644
GIT binary patch
delta 881
zcmV-%1CIRJ1^NdVP)h>@6aWAK2mqr}gITt(ol7PI000~akr+UKRXMI4M-0t%miB;K
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delta 590
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index cb870e2e418972fcdc950784c372754b760ba304..58c895801984501a528812ca959f7c82f1a1398b 100644
GIT binary patch
delta 868
zcmV-q1DpKE1?>kIP)h>@6aWAK2mqr}gIT<oEr}Te008s{kr+UKRk_aQR1EFsSwiG}
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uF4@fe?SD{90Rj{Q6aWAK2mqr}gIT<oEr}Te008s{lVk#225<ua0001Pjir+S

delta 572
zcmV-C0>l072gd~%P)h>@6aWAK2mpauc3G%pbae9p008L(kr+UKR6DK|K@7~_S%#MX
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K21)_|00004_Wbq$

diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 6fac8df2e4dade24a3c85caa59c5464eb7ae3b98..7f8059f92706867c81d1a7dbacb98291fe357b2f 100644
GIT binary patch
delta 1484
zcmV;-1vC242+s@~P)h>@6aWAK2mqr}gIQC8H`;dv0009F0029a`~yCJO_n>ZB{dL+
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delta 964
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index e0b594011c4d2b59ade8aff670982d34e82822b8..125e656d92c96008f86a4e9c81ee32cb5440562b 100644
GIT binary patch
delta 1488
zcmV;>1uy#72-pl5P)h>@6aWAK2mqr}gIVdb%Bg?_000dPkr+aMmQ9XiHx7jNK8sqA
z_$P8XTdxcl@C9rydgB=wzCY$;y5Ca)RRvTkMUlaXAiw`H{`dEve|-P_pYQ+u>!+W8
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delta 971
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 6a8c53cb136895744b66be2b2c9d66e6ae3c74a7..4b121d050dcc439554985c7b9b73b881345a4500 100644
GIT binary patch
delta 1509
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LV+QgC000004eRcw

delta 991
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index 5f35527b2bf080cf740d8f7a5cc31e477ca94bcf..b0d94be5b44484700cb349bcf9731ed033a5cebd 100644
GIT binary patch
delta 1480
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delta 969
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delta 599
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 35937a103e4a4acdbd7ba77b17841394ca20780e..ed7d3c300fd0ed0bd772628f8ed077dac2262a82 100644
GIT binary patch
delta 874
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delta 597
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 571fac7e6e84203d399db178fb8c60b6aa46d58a..c5a1f50e24d7672f1fd60b3139142a847dc14ad6 100644
GIT binary patch
delta 879
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delta 572
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index df4f7a3820f7efc26ba0a35ffef7abf54de652f8..d9e85047f2f05899c2ba70e26be3d25195a4ddb8 100644
GIT binary patch
delta 1491
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delta 959
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index 28ab54bd0af60b0155a9a9fdcd5eb477fc18c897..467c8dc7fc793d50df902bf2c43e32493cb4045e 100644
GIT binary patch
delta 1496
zcmV;}1t<F32;dADP)h>@6aWAK2mqr}gIQ@pH-Lx*000~ekr+aMmQ9XqH4KI4tR+OT
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delta 980
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z_wy{qD%aJuWXlQ(i5Fm4%mxtR{l(`f?M{>^M_tF|s{htMzK?(Y{qy^eKmPmi_g~+B
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diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 17ed54013022e5a5548e3540fb8e731309a207c5..8c7ff797c824731613ee766350024a92ce6addc6 100644
GIT binary patch
delta 1517
zcmV<J1rqxF2>lEgP)h>@6aWAK2mqr}gITFbE<BwD007_&kr+mQmpzVUxebN$Sxa?;
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delta 1000
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W#@w6(002n{lN1JH2KfU300020Rn*S_

diff --git a/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/eqV2_86M_omat_mp_salex/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index 3dffedbe487d88623b446e6ae44d1dbc859027b5..e38f9051f60e2ea7a82f52825eebd87276a9c850 100644
GIT binary patch
delta 1488
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delta 974
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delta 873
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delta 596
zcmV-a0;~P<2igT2P)h>@6aWAK2mpauc3DXkOTY#K0012Y0023YpaVRAO_Vu~RY43z
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diff --git a/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 2d8557befead612a1dc9779d905334051f8f5e50..dff97f278dad356d8aa7e637495f3aa17fc0568a 100644
GIT binary patch
delta 879
zcmV-#1CadN1^5RTP)h>@6aWAK2mqr}gIVZRqDCYG000>Xkr+UKRk^MuMGVd7TbfpR
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delta 592
zcmV-W0<Zn}2ipZ0P)h>@6aWAK2mpauc3CCJEfNO;0015Zkr+UKRJ(3fK@9BkEr%n&
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e2mpauc3CCJEfNO;0015Zla&Kq23`UH0000#PYn40

diff --git a/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 5aa3f2995724bccf2e84397ba47f649bb6be939e..07d17c6ff716dc014f335fd25015e8dbd33f98c9 100644
GIT binary patch
delta 872
zcmV-u1DE{71@Q+MP)h>@6aWAK2mqr}gISn=rePfe008I*kr+UKRXMI5M-0q;mhpgF
za|x9yGGIU#kR1HPHU!-tD$e`Oe%XLvb17EUeEhin|M}04kMF-e{`>cjuiw7>{nww&
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ywcy=(<7e2vqTf(U0Rj{Q6aWAK2mqr}gISn=rePfe008I*lVJi~26O`e0001V)}TWG

delta 574
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M16>A30ssI204d@HV*mgE

diff --git a/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 0c76d21268bf8aa4a4b0a0b4d0da87dea1f33df4..752a8786b97f086588b5637ab89e866d14ea5428 100644
GIT binary patch
delta 1462
zcmV;n1xfnX2)+y$P)h>@6aWAK2mqr}gIV2!W5{L&008C-kr+aMmdlQ8I}L{C=UGDJ
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Q<_nYZ178Ne1poj508<6a6951J

delta 971
zcmV;+12p`;4A%%4P)h>@6aWAK2mpdvc3D@G2S9%V005i^kr+aMRXL7jMG*8kOHC>F
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diff --git a/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index ec4e5653600c4d3e7208915ceffe5cc8d5ea2db3..4b0d7cc173b566ab2a7689d058daec2465bf39c7 100644
GIT binary patch
delta 1481
zcmV;)1vdKK2+<4}P)h>@6aWAK2mqr}gIUl;KnHmR008z2kr+aMmN9O2xeZ18S&J-C
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delta 982
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diff --git a/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 44d9f52ed1873c2115aea040c7272a4aa03eba18..502ad034553f3f213c73085104b5cc9205992f68 100644
GIT binary patch
delta 1492
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delta 991
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diff --git a/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace-alexandria/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index f848e10d9c9291114c709d4a4b5b722d89ce5d55..9e650365cb8f4d955910ea670d54b92ee799945b 100644
GIT binary patch
delta 1451
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delta 963
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delta 588
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diff --git a/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index fda50ffde17eace45a06adaddedabf9b61bcc669..20d038f8c25100f28e1b26d727cd7fb1af8c3fe1 100644
GIT binary patch
delta 883
zcmV-(1C0FC1^fpXP)h>@6aWAK2mqr}gIUM0L@Fo)001Bekr+UKRk^MtG!V@9TZU2J
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delta 585
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diff --git a/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 3c5da28ab9bd5f2d217c103eaea8d247c1016168..002f0d650ed682a1bed859306e1338ef06fed158 100644
GIT binary patch
delta 869
zcmV-r1DgEF1?~qJP)h>@6aWAK2mqr}gIQBo%BmUz008R;kr+UKRk^O%MhxuzEsDVV
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delta 579
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diff --git a/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 7b52cf6b0bcfee3dfaf381167bb473bef74d0144..bc51dfe01c498c2d085968123756ea22f4389f0c 100644
GIT binary patch
delta 1475
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delta 969
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diff --git a/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index 64ef2e7fbfcdf02291e35049f45148f71684d069..3115fe5d145cab23f67de48e64c6164393113209 100644
GIT binary patch
delta 1493
zcmV;`1uFX52;B@AP)h>@6aWAK2mqr}gIN#_RJ(-*000~ekr+aMmb<QHw+)8-Jd2LN
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diff --git a/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 0dae76ad2291c2e40240d182df33f7efd1d2f553..f968fa3b6968e770f041b7fe4bb8f69315ed5adf 100644
GIT binary patch
delta 1523
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diff --git a/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/mace/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index f0e7a85e53c7f929d452036df254ca0542557558..63528c68986e453f61546fb86193e14bf736c0f0 100644
GIT binary patch
delta 1477
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DUfZ9z

delta 587
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 7054dae6fe0cb32d88b604be11726286b7a01015..8c8baa3e5cc9db04b4a4a217a8025a3f3ab8eba2 100644
GIT binary patch
delta 877
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delta 588
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 2965520c7b873273040f280378d38774611af5d4..ffc47d61db201f2183c89ea8dbcc4b44bad02b01 100644
GIT binary patch
delta 860
zcmV-i1Ec)K1>^@9P)h>@6aWAK2mqr}gIP4|HVY5~007npkr+RJO;tOt*hUQOeHKOF
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delta 571
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 1785206fa2215e3609a849dbe4b4413936bbd589..42ebbd292f0b52d5d62f78d52ab5f159607473ad 100644
GIT binary patch
delta 1451
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delta 973
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index f5ab71c2aa20008b75cd10e267dd17a27b49c873..62d2c4c32af1c8a8ce23741f92f17ca04a70a547 100644
GIT binary patch
delta 1456
zcmV;h1yB0d2)GOwP)h>@6aWAK2mqr}gIRa=aHU`c007tvkr+aMmdmakxeZ0<{uWsf
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delta 971
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index ec26c099ead58961f031d0454beadc9600389705..180f131680aa14b61e5c2dd853643b0b65cbb375 100644
GIT binary patch
delta 1479
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delta 995
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index 81099418298bd1964395ecc5361040ec896ba773..ded647cdfccf164dacbbdaf244d03986fbe32006 100644
GIT binary patch
delta 1428
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delta 971
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip
index dff21f2cf050025f26f8da5bf79f440547cf230c..b24011f0d3e056dc80d44bae6971c8b2d66fc5e1 100644
GIT binary patch
delta 1325
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delta 1728
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip
new file mode 100644
index 0000000000000000000000000000000000000000..7b46118e2f9fb6104ad14a5c56f2f30527fbbdca
GIT binary patch
literal 819
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literal 0
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diff --git a/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl-direct/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip
index 03dd5efb1e29d9468b08531304b929b9a4838c50..cf3758803b9048ab58bf44c487d4c5b12d65a6d2 100644
GIT binary patch
delta 1451
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delta 965
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diff --git a/jarvis_leaderboard/contributions/matgl/.DS_Store b/jarvis_leaderboard/contributions/matgl/.DS_Store
deleted file mode 100644
index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 6148
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-a-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-a-dft_3d_chipsff-test-mae.csv.zip
index 0d216a4b47b389f87a287f0fb967eaacb5f149dd..3de2ea26c9d44b54bbb19c39fc7e35c3585bf15d 100644
GIT binary patch
delta 888
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 87873516f4de6f61152cbe079e672c8d4968f8c8..c63f9ea6db92e7faeca540c4c26568c8a5724ca4 100644
GIT binary patch
delta 889
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index 773b7ad4adba6b772e820c41dedd386d708c6c0f..7995fa09547978e168274f07dbf4b0b5999ed46e 100644
GIT binary patch
delta 877
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 0985fc18b9a48223b368b8d106d98f85e0e39659..5f783792a522711885bf234a59fabb9f9f33ff1a 100644
GIT binary patch
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index f7919f2b6e5d4f6f18eb3d0f24857a2101d52660..8499733c999b5943bca7a1c3742ca95fbc60e2de 100644
GIT binary patch
delta 1513
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delta 966
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index e57b8777802562286dd1451a5f1e5173fe723634..7937ebff36697368032aa364757a0bccfc857124 100644
GIT binary patch
delta 1520
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delta 1000
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index 495d4ea8aa74d5b38565a82af4e5794f5ae70d1f..d90defa85fdcea1c65100c1043a771c82745e65c 100644
GIT binary patch
delta 1491
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delta 973
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diff --git a/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/matgl/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip
index f8b57b25d108cf7003cd75985958ea549680ca3a..ed08bd5926ada7b6b209e6e4d56358e4344ddd2d 100644
GIT binary patch
delta 1351
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diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/metadata.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/metadata.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/metadata.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
new file mode 100644
index 000000000..2050bcd3f
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
@@ -0,0 +1,8 @@
+{
+    "calculator_type": "alignn_ff_12_2_24",
+    "mlearn_elements": [
+        "Cu"
+    ],
+    "forces_mae_Cu": 0.21592027727012433,
+    "stresses_mae_Cu": 2.2145441603034666
+}
\ No newline at end of file
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/mlearn_analysis_log.txt
new file mode 100644
index 000000000..77096e93c
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/mlearn_analysis_log.txt
@@ -0,0 +1,39 @@
+2024-12-03 16:35:00,376 - INFO - Filtered dataset to 293 entries containing element 'Cu'
+2024-12-03 16:35:07,397 - INFO - Processed 1/293 structures.
+2024-12-03 16:35:15,377 - INFO - Processed 11/293 structures.
+2024-12-03 16:35:23,369 - INFO - Processed 21/293 structures.
+2024-12-03 16:35:31,167 - INFO - Processed 31/293 structures.
+2024-12-03 16:35:35,574 - INFO - Processed 41/293 structures.
+2024-12-03 16:35:40,962 - INFO - Processed 51/293 structures.
+2024-12-03 16:35:47,479 - INFO - Processed 61/293 structures.
+2024-12-03 16:35:54,676 - INFO - Processed 71/293 structures.
+2024-12-03 16:36:01,255 - INFO - Processed 81/293 structures.
+2024-12-03 16:36:08,268 - INFO - Processed 91/293 structures.
+2024-12-03 16:36:14,984 - INFO - Processed 101/293 structures.
+2024-12-03 16:36:22,058 - INFO - Processed 111/293 structures.
+2024-12-03 16:36:28,342 - INFO - Processed 121/293 structures.
+2024-12-03 16:36:34,290 - INFO - Processed 131/293 structures.
+2024-12-03 16:36:40,901 - INFO - Processed 141/293 structures.
+2024-12-03 16:36:47,653 - INFO - Processed 151/293 structures.
+2024-12-03 16:36:53,432 - INFO - Processed 161/293 structures.
+2024-12-03 16:36:59,260 - INFO - Processed 171/293 structures.
+2024-12-03 16:37:05,451 - INFO - Processed 181/293 structures.
+2024-12-03 16:37:10,698 - INFO - Processed 191/293 structures.
+2024-12-03 16:37:15,898 - INFO - Processed 201/293 structures.
+2024-12-03 16:37:20,620 - INFO - Processed 211/293 structures.
+2024-12-03 16:37:25,270 - INFO - Processed 221/293 structures.
+2024-12-03 16:37:29,780 - INFO - Processed 231/293 structures.
+2024-12-03 16:37:35,096 - INFO - Processed 241/293 structures.
+2024-12-03 16:37:40,850 - INFO - Processed 251/293 structures.
+2024-12-03 16:37:46,341 - INFO - Processed 261/293 structures.
+2024-12-03 16:37:51,072 - INFO - Processed 271/293 structures.
+2024-12-03 16:37:56,916 - INFO - Processed 281/293 structures.
+2024-12-03 16:38:02,651 - INFO - Processed 291/293 structures.
+2024-12-03 16:38:03,955 - INFO - Saved force comparison data to 'mlearn_analysis_Cu_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Cu-test-multimae.csv'
+2024-12-03 16:38:03,961 - INFO - Saved stress comparison data to 'mlearn_analysis_Cu_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Cu-test-multimae.csv'
+2024-12-03 16:38:04,209 - INFO - Zipped data to 'mlearn_analysis_Cu_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Cu-test-multimae.csv.zip'
+2024-12-03 16:38:04,219 - INFO - Zipped data to 'mlearn_analysis_Cu_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Cu-test-multimae.csv.zip'
+2024-12-03 16:38:04,264 - INFO - Forces MAE for element 'Cu': 0.215920 eV/Å
+2024-12-03 16:38:04,269 - INFO - Stresses MAE for element 'Cu': 2.214544 GPa
+2024-12-03 16:38:07,828 - INFO - Saved parity plot for Forces as 'mlearn_analysis_Cu_alignn_ff_12_2_24/forces_parity_plot_Cu.png'
+2024-12-03 16:38:08,554 - INFO - Saved parity plot for Stresses as 'mlearn_analysis_Cu_alignn_ff_12_2_24/stresses_parity_plot_Cu.png'
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/run.sh
new file mode 100644
index 000000000..0e331ca03
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/run.sh
@@ -0,0 +1,148 @@
+#!/bin/bash
+
+# Create logs directory if it doesn't exist
+mkdir -p logs
+
+# Define arrays of JIDs, calculators, and mlearn elements
+calculator_types=("alignn_ff_12_2_24")
+mlearn_elements=("Si" "Ge" "Cu" "Ni" "Li" "Mo")
+
+# Function to submit a job for bulk material calculations
+submit_bulk_job() {
+  local jid=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=30-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=${jid}_${calculator}
+#SBATCH --output=logs/${jid}_${calculator}_%j.out
+#SBATCH --error=logs/${jid}_${calculator}_%j.err
+
+# Generate input JSON file for this combination
+cat > input_${jid}_${calculator}.json <<JSON
+{
+  "jid": "$jid",
+  "calculator_type": "$calculator",
+  "chemical_potentials_file": "chemical_potentials.json",
+  "properties_to_calculate": [
+    "relax_structure",
+    "calculate_ev_curve",
+    "calculate_formation_energy",
+    "calculate_elastic_tensor",
+    "run_phonon_analysis",
+    "analyze_surfaces",
+    "analyze_defects",
+    "run_phonon3_analysis",
+    "general_melter",
+    "calculate_rdf"
+  ],
+  "bulk_relaxation_settings": {
+    "filter_type": "ExpCellFilter",
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": false
+    }
+  },
+  "phonon_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "use_conventional_cell": true,
+  "surface_settings": {
+    "indices_list": [
+      [0, 1, 0],
+      [0, 0, 1]
+    ],
+    "layers": 4,
+    "vacuum": 18,
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "defect_settings": {
+    "generate_settings": {
+      "on_conventional_cell": true,
+      "enforce_c_size": 8,
+      "extend": 1
+    },
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "phonon3_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "md_settings": {
+    "dt": 1,
+    "temp0": 35,
+    "nsteps0": 10,
+    "temp1": 200,
+    "nsteps1": 20,
+    "taut": 20,
+    "min_size": 10.0
+  }
+}
+JSON
+
+# Run the Python analysis for this JID/calculator combination
+python run_chipsff.py --input_file input_${jid}_${calculator}.json
+
+EOT
+}
+
+# Function to submit a job for mlearn analysis
+submit_mlearn_job() {
+  local element=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=1-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=mlearn_${element}_${calculator}
+#SBATCH --output=logs/mlearn_${element}_${calculator}_%j.out
+#SBATCH --error=logs/mlearn_${element}_${calculator}_%j.err
+
+# Generate input JSON file for this mlearn element/calculator combination
+cat > input_mlearn_${element}_${calculator}.json <<JSON
+{
+  "mlearn_elements": ["$element"],
+  "calculator_type": "$calculator",
+  "properties_to_calculate": ["compare_mlearn_forces"]
+}
+JSON
+
+# Run the Python analysis for this mlearn element/calculator combination
+python run_chipsff.py --input_file input_mlearn_${element}_${calculator}.json
+
+EOT
+}
+
+# Loop through each JID and calculator combination for bulk calculations
+for jid in "${jid_list[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_bulk_job "$jid" "$calculator"
+  done
+done
+
+# Loop through each mlearn element and calculator combination for mlearn analysis
+for element in "${mlearn_elements[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_mlearn_job "$element" "$calculator"
+  done
+done
+
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/run_chipsff.py
new file mode 100644
index 000000000..79b65eb07
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_12_2_24/run_chipsff.py
@@ -0,0 +1,2286 @@
+#!/usr/bin/env python
+import os
+import subprocess
+import sys
+import time
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from ase.eos import EquationOfState
+from ase import Atoms as AseAtoms
+from ase.units import kJ
+from ase.constraints import ExpCellFilter
+from ase.optimize.fire import FIRE
+from ase.stress import voigt_6_to_full_3x3_stress
+import ase.units
+from jarvis.db.figshare import get_jid_data, data, get_request_data
+from jarvis.core.atoms import Atoms, ase_to_atoms
+from jarvis.io.vasp.inputs import Poscar
+from jarvis.core.kpoints import Kpoints3D as Kpoints
+from jarvis.analysis.defects.vacancy import Vacancy
+from jarvis.analysis.defects.surface import Surface
+import pandas as pd
+import h5py
+import shutil
+import glob
+import io
+import contextlib
+import requests
+import zipfile
+import re
+import plotly.express as px
+from sklearn.metrics import mean_absolute_error, r2_score
+from matplotlib.gridspec import GridSpec
+import argparse
+from jarvis.db.jsonutils import loadjson
+from chipsff.config import CHIPSFFConfig
+
+dft_3d = data("dft_3d")
+vacancydb = data("vacancydb")
+surf_url = "https://figshare.com/ndownloader/files/46355689"
+surface_data = get_request_data(js_tag="surface_db_dd.json", url=surf_url)
+
+def get_entry(jid):
+    for entry in dft_3d:
+        if entry["jid"] == jid:
+            return entry
+    raise ValueError(f"JID {jid} not found in the database")
+
+def collect_data(dft_3d, vacancydb, surface_data):
+    defect_ids = list(set([entry["jid"] for entry in vacancydb]))
+    surf_ids = list(set([entry["name"].split("Surface-")[1].split("_miller_")[0] for entry in surface_data]))
+
+    aggregated_data = []
+    for entry in dft_3d:
+        tmp = entry
+        tmp["vacancy"] = {}
+        tmp["surface"] = {}
+
+        # Check if the entry is in the defect dataset
+        if entry["jid"] in defect_ids:
+            for vac_entry in vacancydb:
+                if entry["jid"] == vac_entry["jid"]:
+                    tmp["vacancy"].setdefault(
+                        vac_entry["id"].split("_")[0] + "_" + vac_entry["id"].split("_")[1],
+                        vac_entry["ef"]
+                    )
+
+        # Check if the entry is in the surface dataset
+        if entry["jid"] in surf_ids:
+            for surf_entry in surface_data:
+                jid = surf_entry["name"].split("Surface-")[1].split("_miller_")[0]
+                if entry["jid"] == jid:
+                    tmp["surface"].setdefault(
+                        "_".join(surf_entry["name"].split("_thickness")[0].split("_")[0:5]),
+                        surf_entry["surf_en"]
+                    )
+
+        aggregated_data.append(tmp)
+    
+    return aggregated_data
+
+def get_vacancy_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the vacancy formation energy entry (vac_en_entry) for a given jid.
+    
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+    
+    Returns:
+    dict: A dictionary containing the vacancy formation energy entry and corresponding symbol.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            vacancy_data = entry.get("vacancy", {})
+            if vacancy_data:
+                return [{"symbol": key, "vac_en_entry": value} for key, value in vacancy_data.items()]
+            else:
+                return f"No vacancy data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+    
+def get_surface_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the surface energy entry (surf_en_entry) for a given jid.
+
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+
+    Returns:
+    list: A list of dictionaries containing the surface energy entry and corresponding name.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            surface_data = entry.get("surface", {})
+            if surface_data:
+                # Prepend 'Surface-JVASP-<jid>_' to the key for correct matching
+                return [{"name": f"{key}", "surf_en_entry": value} for key, value in surface_data.items()]
+            else:
+                return f"No surface data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+
+
+def log_job_info(message, log_file):
+    """Log job information to a file and print it."""
+    with open(log_file, "a") as f:
+        f.write(message + "\n")
+    print(message)
+
+def save_dict_to_json(data_dict, filename):
+    with open(filename, "w") as f:
+        json.dump(data_dict, f, indent=4)
+
+def load_dict_from_json(filename):
+    with open(filename, 'r') as f:
+        return json.load(f)
+        
+def setup_calculator(calculator_type):
+    if calculator_type == "matgl":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "matgl-direct":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-DIRECT-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "alignn_ff":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+        model_path = default_path() #can be adjusted to other ALIGNN models
+        return AlignnAtomwiseCalculator(
+            path=model_path,
+            stress_wt=0.1,
+            force_mult_natoms=False,
+            force_multiplier=1,
+            modl_filename="best_model.pt",
+        )
+    elif calculator_type == "alignn_ff_12_2_24":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+        return AlignnAtomwiseCalculator()
+    elif calculator_type == "chgnet":
+        from chgnet.model.dynamics import CHGNetCalculator
+        return CHGNetCalculator()
+    elif calculator_type == "mace":
+        from mace.calculators import mace_mp
+        return mace_mp()
+    elif calculator_type == "mace-alexandria":
+        from mace.calculators.mace import MACECalculator
+        model_path="/users/dtw2/utils/models/alexandria_v2/mace/2D_universal_force_field_cpu.model" #adjust path to mace-alexandria
+        return MACECalculator(model_path,device="cpu") 
+    elif calculator_type == "sevennet":
+        from sevenn.sevennet_calculator import SevenNetCalculator
+        checkpoint_path = "/users/dtw2/SevenNet/pretrained_potentials/SevenNet_0__11July2024/checkpoint_sevennet_0.pth" #adjust path to sevennet
+        return SevenNetCalculator(checkpoint_path, device="cpu")
+    elif calculator_type == "orb-v2":
+        from orb_models.forcefield import pretrained
+        from orb_models.forcefield.calculator import ORBCalculator
+        orbff = pretrained.orb_v2()
+        return ORBCalculator(orbff, device="cpu")
+    elif calculator_type == "eqV2_31M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat.pt") #adjust path to OMat24   
+    elif calculator_type == "eqV2_153M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_153M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_31M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat_mp_salex.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat_mp_salex.pt") #adjust path to OMat24
+    else:
+        raise ValueError("Unsupported calculator type")
+
+class MaterialsAnalyzer:
+    def __init__(
+        self,
+        jid=None,
+        calculator_type=None,
+        chemical_potentials_file=None,
+        film_jid=None,
+        substrate_jid=None,
+        film_index=None,
+        substrate_index=None,
+        bulk_relaxation_settings=None,
+        phonon_settings=None,
+        properties_to_calculate=None,
+        use_conventional_cell=False,
+        surface_settings=None,
+        defect_settings=None,
+        phonon3_settings=None,
+        md_settings=None
+    ):
+        self.calculator_type = calculator_type
+        self.use_conventional_cell = use_conventional_cell
+        self.chemical_potentials_file = chemical_potentials_file
+        self.bulk_relaxation_settings = bulk_relaxation_settings or {}
+        self.phonon_settings = phonon_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.properties_to_calculate = properties_to_calculate or []
+        self.surface_settings = surface_settings or {}
+        self.defect_settings = defect_settings or {}
+        self.film_index = film_index or "1_1_0"
+        self.substrate_index = substrate_index or "1_1_0"
+        self.phonon3_settings = phonon3_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.md_settings = md_settings or {'dt': 1, 'temp0': 3500, 'nsteps0': 1000, 'temp1': 300, 'nsteps1': 2000, 'taut': 20, 'min_size': 10.0}
+        if jid:
+            self.jid = jid
+            # Load atoms for the given JID
+            self.atoms = self.get_atoms(jid)
+            # Get reference data for the material
+            self.reference_data = get_entry(jid)
+            # Set up the output directory and log file
+            self.output_dir = f"{jid}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(self.output_dir, f"{jid}_job_log.txt")
+            # Initialize job_info dictionary
+            self.job_info = {
+                "jid": jid,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        elif film_jid and substrate_jid:
+            # Ensure film_jid and substrate_jid are strings, not lists
+            if isinstance(film_jid, list):
+                film_jid = film_jid[0]
+            if isinstance(substrate_jid, list):
+                substrate_jid = substrate_jid[0]
+
+            self.film_jid = film_jid
+            self.substrate_jid = substrate_jid
+
+            # Include Miller indices in directory and file names
+            self.output_dir = f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(
+                self.output_dir,
+                f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_job_log.txt",
+            )
+            self.job_info = {
+                "film_jid": film_jid,
+                "substrate_jid": substrate_jid,
+                "film_index": self.film_index,
+                "substrate_index": self.substrate_index,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        else:
+            raise ValueError("Either 'jid' or both 'film_jid' and 'substrate_jid' must be provided.")
+
+        # Set up the logger
+        self.setup_logger()
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger(self.jid or f"{self.film_jid}_{self.substrate_jid}")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        """Log information to the job log file."""
+        log_job_info(message, self.log_file)
+
+    def get_atoms(self, jid):
+        dat = get_jid_data(jid=jid, dataset="dft_3d")
+        return Atoms.from_dict(dat["atoms"])
+
+    def setup_calculator(self):
+        calc = setup_calculator(self.calculator_type)
+        self.log(f"Using calculator: {self.calculator_type}")
+        return calc
+
+    def load_chemical_potentials(self):
+        if os.path.exists(self.chemical_potentials_file):
+            with open(self.chemical_potentials_file, "r") as f:
+                return json.load(f)
+        else:
+            return {}
+
+    def save_chemical_potentials(self):
+        with open(self.chemical_potentials_file, "w") as f:
+            json.dump(self.chemical_potentials, f, indent=4)
+
+    def capture_fire_output(self, ase_atoms, fmax, steps):
+        """Capture the output of the FIRE optimizer."""
+        log_stream = io.StringIO()
+        with contextlib.redirect_stdout(log_stream):
+            dyn = FIRE(ase_atoms)
+            dyn.run(fmax=fmax, steps=steps)
+        output = log_stream.getvalue().strip()
+
+        final_energy = None
+        if output:
+            last_line = output.split("\n")[-1]
+            match = re.search(r"FIRE:\s+\d+\s+\d+:\d+:\d+\s+(-?\d+\.\d+)", last_line)
+            if match:
+                final_energy = float(match.group(1))
+
+        return final_energy, dyn.nsteps
+
+    def relax_structure(self):
+        """Perform structure relaxation and log the process."""
+        self.log(f"Starting relaxation for {self.jid}")
+
+        # Use conventional cell if specified
+
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for relaxation.")
+            self.atoms = self.atoms.get_conventional_atoms  # or appropriate method
+
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.bulk_relaxation_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.bulk_relaxation_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', False)
+        ase_atoms = self.atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+
+        # Run FIRE optimizer and capture the output using relaxation settings
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < steps
+
+        # Log the final energy and relaxation status
+        self.log(f"Final energy of FIRE optimization for structure: {final_energy}")
+        self.log(
+            f"Relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        # Update job info and save the relaxed structure
+        self.job_info["relaxed_atoms"] = relaxed_atoms.to_dict()
+        self.job_info["final_energy_structure"] = final_energy
+        self.job_info["converged"] = converged
+        self.log(f"Relaxed structure: {relaxed_atoms.to_dict()}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return relaxed_atoms if converged else None
+
+
+
+    def calculate_formation_energy(self, relaxed_atoms):
+        """
+        Calculate the formation energy per atom using the equilibrium energy and chemical potentials.
+        """
+        e0 = self.job_info["equilibrium_energy"]
+        composition = relaxed_atoms.composition.to_dict()
+        total_energy = e0
+
+        for element, amount in composition.items():
+            chemical_potential = self.get_chemical_potential(element)
+            if chemical_potential is None:
+                self.log(f"Skipping formation energy calculation due to missing chemical potential for {element}.")
+                continue  # Or handle this appropriately
+            total_energy -= chemical_potential * amount
+
+        formation_energy_per_atom = total_energy / relaxed_atoms.num_atoms
+
+    # Log and save the formation energy
+        self.job_info["formation_energy_per_atom"] = formation_energy_per_atom
+        self.log(f"Formation energy per atom: {formation_energy_per_atom}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return formation_energy_per_atom
+
+    def calculate_element_chemical_potential(self, element, element_jid):
+        """
+        Calculate the chemical potential of a pure element using its standard structure.
+        """
+        self.log(f"Calculating chemical potential for element: {element} using JID: {element_jid}")
+        try:
+            # Get standard structure for the element using the provided JID
+            element_atoms = self.get_atoms(element_jid)
+            ase_atoms = element_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+
+            # Perform energy calculation
+            energy = ase_atoms.get_potential_energy() / len(ase_atoms)
+            self.log(f"Calculated chemical potential for {element}: {energy} eV/atom")
+            return energy
+        except Exception as e:
+            self.log(f"Error calculating chemical potential for {element}: {e}")
+            return None
+
+    def get_chemical_potential(self, element):
+        """Fetch chemical potential from JSON based on the element and calculator."""
+        element_data = self.chemical_potentials.get(element, {})
+        chemical_potential = element_data.get(f"energy_{self.calculator_type}")
+
+        if chemical_potential is None:
+            self.log(
+                f"No chemical potential found for {element} with calculator {self.calculator_type}, calculating it now..."
+            )
+            # Get standard JID for the element from chemical_potentials.json
+            element_jid = element_data.get("jid")
+            if element_jid is None:
+                self.log(f"No standard JID found for element {element} in chemical_potentials.json")
+                return None  # Skip this element
+
+            # Calculate chemical potential
+            chemical_potential = self.calculate_element_chemical_potential(element, element_jid)
+            if chemical_potential is None:
+                self.log(f"Failed to calculate chemical potential for {element}")
+                return None
+            # Add it to the chemical potentials dictionary
+            if element not in self.chemical_potentials:
+                self.chemical_potentials[element] = {}
+            self.chemical_potentials[element][f"energy_{self.calculator_type}"] = chemical_potential
+            # Save the updated chemical potentials to file
+            self.save_chemical_potentials()
+
+        return chemical_potential
+    def calculate_forces(self, atoms):
+        """
+        Calculate the forces on the given atoms without performing relaxation.
+        """
+        self.log(f"Calculating forces for {self.jid}")
+
+    # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+    # Calculate forces
+        forces = ase_atoms.get_forces()  # This returns an array of forces
+
+    # Log and save the forces
+        self.job_info['forces'] = forces.tolist()  # Convert to list for JSON serialization
+        self.log(f"Forces calculated: {forces}")
+
+    # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return forces
+
+    def calculate_ev_curve(self, relaxed_atoms):
+        """Calculate the energy-volume (E-V) curve and log results."""
+        self.log(f"Calculating EV curve for {self.jid}")
+
+        dx = np.arange(-0.06, 0.06, 0.01)  # Strain values
+        y = []  # Energies
+        vol = []  # Volumes
+        strained_structures = []  # To store strained structures
+
+        for i in dx:
+            # Apply strain and calculate energy at each strain value
+            strained_atoms = relaxed_atoms.strain_atoms(i)
+            strained_structures.append(strained_atoms)
+            ase_atoms = strained_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+            energy = ase_atoms.get_potential_energy()
+
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+        # Convert data to numpy arrays for processing
+        y = np.array(y)
+        vol = np.array(vol)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        try:
+            eos = EquationOfState(vol, y, eos="murnaghan")
+            v0, e0, B = eos.fit()
+
+            # Bulk modulus in GPa (conversion factor from ASE units)
+            kv = B / kJ * 1.0e24  # Convert to GPa
+
+            # Log important results
+            self.log(f"Bulk modulus: {kv} GPa")
+            self.log(f"Equilibrium energy: {e0} eV")
+            self.log(f"Equilibrium volume: {v0} ų")
+
+            # Save E-V curve plot
+            fig = plt.figure()
+            eos.plot()
+            ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+            fig.savefig(ev_plot_filename)
+            plt.close(fig)
+            self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+            # Save E-V curve data to a text file
+            ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+            with open(ev_data_filename, "w") as f:
+                f.write("Volume (ų)\tEnergy (eV)\n")
+                for v, e in zip(vol, y):
+                    f.write(f"{v}\t{e}\n")
+            self.log(f"E-V curve data saved to {ev_data_filename}")
+
+            # Update job info with the results
+            self.job_info["bulk_modulus"] = kv
+            self.job_info["equilibrium_energy"] = e0
+            self.job_info["equilibrium_volume"] = v0
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        except RuntimeError as e:
+            self.log(f"Error fitting EOS for {self.jid}: {e}")
+            self.log("Skipping bulk modulus calculation due to fitting error.")
+            kv = None  # Set bulk modulus to None or handle this as you wish
+            e0, v0 = None, None  # Set equilibrium energy and volume to None
+
+        # Return additional values for thermal expansion analysis
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+
+    def calculate_elastic_tensor(self, relaxed_atoms):
+        import elastic
+        from elastic import get_elementary_deformations, get_elastic_tensor
+        """
+        Calculate the elastic tensor for the relaxed structure using the provided calculator.
+        """
+        self.log(f"Starting elastic tensor calculation for {self.jid}")
+        start_time = time.time()
+
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Get elementary deformations for elastic tensor calculation
+        systems = elastic.get_elementary_deformations(ase_atoms)
+
+        # Calculate the elastic tensor and convert to GPa
+        cij_order = elastic.elastic.get_cij_order(ase_atoms)
+        Cij, Bij = elastic.get_elastic_tensor(ase_atoms, systems)
+        elastic_tensor = {
+            i: j / ase.units.GPa for i, j in zip(cij_order, Cij)
+        }  # Convert to GPa
+
+        # Save and log the results
+        self.job_info["elastic_tensor"] = elastic_tensor
+        self.log(
+            f"Elastic tensor for {self.jid} with {self.calculator_type}: {elastic_tensor}"
+        )
+
+        # Timing the calculation
+        end_time = time.time()
+        self.log(f"Elastic Calculation time: {end_time - start_time} seconds")
+
+        # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return elastic_tensor
+
+    def run_phonon_analysis(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Perform Phonon calculation, generate force constants, and plot band structure & DOS."""
+        self.log(f"Starting phonon analysis for {self.jid}")
+        phonopy_bands_figname = f"ph_{self.jid}_{self.calculator_type}.png"
+
+        # Phonon generation parameters
+        dim = self.phonon_settings.get('dim', [2, 2, 2])
+        # Define the conversion factor from THz to cm^-1
+        THz_to_cm = 33.35641  # 1 THz = 33.35641 cm^-1
+
+        force_constants_filename = "FORCE_CONSTANTS"
+        eigenvalues_filename = "phonon_eigenvalues.txt"
+        thermal_props_filename = "thermal_properties.txt"
+        write_fc = True
+        min_freq_tol_cm = -5.0  # in cm^-1
+        distance = self.phonon_settings.get('distance', 0.2)
+
+        # Generate k-point path
+        kpoints = Kpoints().kpath(relaxed_atoms, line_density=5)
+
+        # Convert atoms to Phonopy-compatible object
+        self.log("Converting atoms to Phonopy-compatible format...")
+        bulk = relaxed_atoms.phonopy_converter()
+        phonon = Phonopy(
+            bulk,
+            [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]],
+            # Do not set factor here to keep frequencies in THz during calculations
+        )
+
+        # Generate displacements
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+        self.log(f"Generated {len(supercells)} supercells for displacements.")
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for idx, scell in enumerate(supercells):
+            self.log(f"Calculating forces for supercell {idx+1}...")
+            ase_atoms = AseAtoms(
+                symbols=scell.symbols,
+                positions=scell.positions,
+                cell=scell.cell,
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+
+            # Correct for drift force
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+
+            set_of_forces.append(forces)
+
+        # Generate force constants
+        self.log("Producing force constants...")
+        phonon.produce_force_constants(forces=set_of_forces)
+
+        # Write force constants to file if required
+        if write_fc:
+            force_constants_filepath = os.path.join(
+                self.output_dir, force_constants_filename
+            )
+            self.log(f"Writing force constants to {force_constants_filepath}...")
+            write_FORCE_CONSTANTS(
+                phonon.force_constants, filename=force_constants_filepath
+            )
+            self.log(f"Force constants saved to {force_constants_filepath}")
+
+        # Prepare bands for band structure calculation
+        bands = [kpoints.kpts]  # Assuming kpoints.kpts is a list of q-points
+
+        # Prepare labels and path_connections
+        labels = []
+        path_connections = []
+        for i, label in enumerate(kpoints.labels):
+            if label:
+                labels.append(label)
+            else:
+                labels.append("")  # Empty string for points without labels
+
+        # Since we have a single path, set path_connections accordingly
+        path_connections = [True] * (len(bands) - 1)
+        path_connections.append(False)
+
+        # Run band structure calculation with labels
+        self.log("Running band structure calculation...")
+        phonon.run_band_structure(
+            bands,
+            with_eigenvectors=False,
+            labels=labels,
+            path_connections=path_connections,
+        )
+
+        # Write band.yaml file (frequencies will be in THz)
+        band_yaml_filepath = os.path.join(self.output_dir, "band.yaml")
+        self.log(f"Writing band structure data to {band_yaml_filepath}...")
+        phonon.band_structure.write_yaml(filename=band_yaml_filepath)
+        self.log(f"band.yaml saved to {band_yaml_filepath}")
+
+        # --- Begin post-processing to convert frequencies to cm^-1 while preserving formatting ---
+        from ruamel.yaml import YAML
+
+        self.log(f"Converting frequencies in {band_yaml_filepath} to cm^-1 while preserving formatting...")
+        yaml = YAML()
+        yaml.preserve_quotes = True
+
+        with open(band_yaml_filepath, 'r') as f:
+            band_data = yaml.load(f)
+
+        for phonon_point in band_data['phonon']:
+            for band in phonon_point['band']:
+                freq = band['frequency']
+                if freq is not None:
+                    band['frequency'] = freq * THz_to_cm
+
+        with open(band_yaml_filepath, 'w') as f:
+            yaml.dump(band_data, f)
+
+        self.log(f"Frequencies in {band_yaml_filepath} converted to cm^-1 with formatting preserved")
+        # --- End post-processing ---
+
+        # Phonon band structure and eigenvalues
+        lbls = kpoints.labels
+        lbls_ticks = []
+        freqs = []
+        lbls_x = []
+        count = 0
+        eigenvalues = []
+
+        for ii, k in enumerate(kpoints.kpts):
+            k_str = ",".join(map(str, k))
+            if ii == 0 or k_str != ",".join(map(str, kpoints.kpts[ii - 1])):
+                freqs_at_k = phonon.get_frequencies(k)  # Frequencies in THz
+                freqs_at_k_cm = freqs_at_k * THz_to_cm  # Convert to cm^-1
+                freqs.append(freqs_at_k_cm)
+                eigenvalues.append((k, freqs_at_k_cm))  # Store frequencies in cm^-1
+                lbl = "$" + str(lbls[ii]) + "$" if lbls[ii] else ""
+                if lbl:
+                    lbls_ticks.append(lbl)
+                    lbls_x.append(count)
+                count += 1
+
+        # Write eigenvalues to file with frequencies in cm^-1
+        eigenvalues_filepath = os.path.join(self.output_dir, eigenvalues_filename)
+        self.log(f"Writing phonon eigenvalues to {eigenvalues_filepath}...")
+        with open(eigenvalues_filepath, "w") as eig_file:
+            eig_file.write("k-points\tFrequencies (cm^-1)\n")
+            for k, freqs_at_k_cm in eigenvalues:
+                k_str = ",".join(map(str, k))
+                freqs_str = "\t".join(map(str, freqs_at_k_cm))
+                eig_file.write(f"{k_str}\t{freqs_str}\n")
+        self.log(f"Phonon eigenvalues saved to {eigenvalues_filepath}")
+
+        # Convert frequencies to numpy array in cm^-1
+        freqs = np.array(freqs)
+
+        # Plot phonon band structure and DOS
+        the_grid = plt.GridSpec(1, 2, width_ratios=[3, 1], wspace=0.0)
+        plt.rcParams.update({"font.size": 18})
+        plt.figure(figsize=(10, 5))
+
+        # Plot phonon bands
+        plt.subplot(the_grid[0])
+        for i in range(freqs.shape[1]):
+            plt.plot(freqs[:, i], lw=2, c="b")
+        for i in lbls_x:
+            plt.axvline(x=i, c="black")
+        plt.xticks(lbls_x, lbls_ticks)
+        plt.ylabel("Frequency (cm$^{-1}$)")
+        plt.xlim([0, max(lbls_x)])
+
+        # Run mesh and DOS calculations
+        phonon.run_mesh([40, 40, 40], is_gamma_center=True, is_mesh_symmetry=False)
+        phonon.run_total_dos()
+        tdos = phonon.total_dos
+        freqs_dos = np.array(tdos.frequency_points) * THz_to_cm  # Convert to cm^-1
+        dos_values = tdos.dos
+        min_freq = min_freq_tol_cm  # in cm^-1
+        max_freq = max(freqs_dos)
+
+        plt.ylim([min_freq, max_freq])
+
+        # Plot DOS
+        plt.subplot(the_grid[1])
+        plt.fill_between(
+            dos_values, freqs_dos, color=(0.2, 0.4, 0.6, 0.6), edgecolor="k", lw=1, y2=0
+        )
+        plt.xlabel("DOS")
+        plt.yticks([])
+        plt.xticks([])
+        plt.ylim([min_freq, max_freq])
+        plt.xlim([0, max(dos_values)])
+
+        # Save the plot
+        os.makedirs(self.output_dir, exist_ok=True)
+        plot_filepath = os.path.join(self.output_dir, phonopy_bands_figname)
+        plt.tight_layout()
+        plt.savefig(plot_filepath)
+        self.log(
+            f"Phonon band structure and DOS combined plot saved to {plot_filepath}"
+        )
+        plt.close()
+
+        self.log("Calculating thermal properties...")
+        phonon.run_mesh(mesh=[20, 20, 20])
+        phonon.run_thermal_properties(t_step=10, t_max=1000, t_min=0)
+        tprop_dict = phonon.get_thermal_properties_dict()
+
+        # Plot thermal properties
+        plt.figure()
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["free_energy"],
+            label="Free energy (kJ/mol)",
+            color="red",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["entropy"],
+            label="Entropy (J/K*mol)",
+            color="blue",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["heat_capacity"],
+            label="Heat capacity (J/K*mol)",
+            color="green",
+        )
+        plt.legend()
+        plt.xlabel("Temperature (K)")
+        plt.ylabel("Thermal Properties")
+        plt.title("Thermal Properties")
+
+        thermal_props_plot_filepath = os.path.join(
+            self.output_dir, f"Thermal_Properties_{self.jid}.png"
+        )
+        plt.savefig(thermal_props_plot_filepath)
+        self.log(f"Thermal properties plot saved to {thermal_props_plot_filepath}")
+        plt.close()
+
+        # Save thermal properties to file
+        thermal_props_filepath = os.path.join(self.output_dir, thermal_props_filename)
+        with open(thermal_props_filepath, "w") as f:
+            f.write(
+                "Temperature (K)\tFree Energy (kJ/mol)\tEntropy (J/K*mol)\tHeat Capacity (J/K*mol)\n"
+            )
+            for i in range(len(tprop_dict["temperatures"])):
+                f.write(
+                    f"{tprop_dict['temperatures'][i]}\t{tprop_dict['free_energy'][i]}\t"
+                    f"{tprop_dict['entropy'][i]}\t{tprop_dict['heat_capacity'][i]}\n"
+                )
+        self.log(f"Thermal properties written to {thermal_props_filepath}")
+
+        # Calculate zero-point energy (ZPE)
+        zpe = tprop_dict["free_energy"][0] * 0.0103643  # Converting from kJ/mol to eV
+        self.log(f"Zero-point energy: {zpe} eV")
+
+        # Save to job info
+        self.job_info["phonopy_bands"] = phonopy_bands_figname
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return phonon, zpe
+
+    def analyze_defects(self):
+        """Analyze defects by generating, relaxing, and calculating vacancy formation energy."""
+        self.log("Starting defect analysis...")
+        generate_settings = self.defect_settings.get('generate_settings', {})
+        on_conventional_cell = generate_settings.get('on_conventional_cell', True)
+        enforce_c_size = generate_settings.get('enforce_c_size', 8)
+        extend = generate_settings.get('extend', 1)
+    # Generate defect structures from the original atoms
+        defect_structures = Vacancy(self.atoms).generate_defects(on_conventional_cell=on_conventional_cell, enforce_c_size=enforce_c_size, extend=extend)
+
+        for defect in defect_structures:
+        # Extract the defect structure and related metadata
+            defect_structure = Atoms.from_dict(defect.to_dict()["defect_structure"])
+        
+        # Construct a consistent defect name without Wyckoff notation
+            element = defect.to_dict()['symbol']
+            defect_name = f"{self.jid}_{element}"  # Consistent format
+            self.log(f"Analyzing defect: {defect_name}")
+
+        # Relax the defect structure
+            relaxed_defect_atoms = self.relax_defect_structure(defect_structure, name=defect_name)
+
+            if relaxed_defect_atoms is None:
+                self.log(f"Skipping {defect_name} due to failed relaxation.")
+                continue
+
+        # Retrieve energies for calculating the vacancy formation energy
+            vacancy_energy = self.job_info.get(f"final_energy_defect for {defect_name}")
+            bulk_energy = (
+                self.job_info.get("equilibrium_energy")
+                / self.atoms.num_atoms
+                * (defect_structure.num_atoms + 1)
+            )
+
+            if vacancy_energy is None or bulk_energy is None:
+                self.log(f"Skipping {defect_name} due to missing energy values.")
+                continue
+
+        # Get chemical potential and calculate vacancy formation energy
+            chemical_potential = self.get_chemical_potential(element)
+
+            if chemical_potential is None:
+                self.log(f"Skipping {defect_name} due to missing chemical potential for {element}.")
+                continue
+
+            vacancy_formation_energy = vacancy_energy - bulk_energy + chemical_potential
+
+        # Log and store the vacancy formation energy consistently
+            self.job_info[f"vacancy_formation_energy for {defect_name}"] = vacancy_formation_energy
+            self.log(f"Vacancy formation energy for {defect_name}: {vacancy_formation_energy} eV")
+
+    # Save the job info to a JSON file
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+        self.log("Defect analysis completed.")
+
+    def relax_defect_structure(self, atoms, name):
+        """Relax the defect structure and log the process."""
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.defect_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.defect_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log the final energy and relaxation status
+        self.log(
+            f"Final energy of FIRE optimization for defect structure: {final_energy}"
+        )
+        self.log(
+            f"Defect relaxation {'converged' if converged else 'did not converge'} within 200 steps."
+        )
+
+        # Update job info with the final energy and convergence status
+        self.job_info[f"final_energy_defect for {name}"] = final_energy
+        self.job_info[f"converged for {name}"] = converged
+
+        if converged:
+            poscar_filename = os.path.join(self.output_dir, f"POSCAR_{name}_relaxed.vasp")
+            poscar_defect = Poscar(relaxed_atoms)
+            poscar_defect.write_file(poscar_filename)
+            self.log(f"Relaxed defect structure saved to {poscar_filename}")
+
+        return relaxed_atoms if converged else None
+
+    def analyze_surfaces(self):
+        """
+        Perform surface analysis by generating surface structures, relaxing them, and calculating surface energies.
+        """
+        self.log(f"Analyzing surfaces for {self.jid}")
+
+        indices_list = self.surface_settings.get('indices_list', [
+            [1, 0, 0],
+            [1, 1, 1],
+            [1, 1, 0],
+            [0, 1, 1],
+            [0, 0, 1],
+            [0, 1, 0],
+        ])
+        layers = self.surface_settings.get('layers', 4)
+        vacuum = self.surface_settings.get('vacuum', 18)
+
+        for indices in indices_list:
+            # Generate surface and check for polarity
+            surface = (
+                Surface(atoms=self.atoms, indices=indices, layers=layers, vacuum=vacuum)
+                .make_surface()
+                .center_around_origin()
+            )
+            if surface.check_polar:
+                self.log(
+                    f"Skipping polar surface for {self.jid} with indices {indices}"
+                )
+                continue
+
+            # Write initial POSCAR for surface
+            poscar_surface = Poscar(atoms=surface)
+            poscar_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}.vasp",
+                )
+            )
+
+            # Relax the surface structure
+            relaxed_surface_atoms, final_energy = self.relax_surface_structure(
+                surface, indices
+            )
+
+            # If relaxation failed, skip further calculations
+            if relaxed_surface_atoms is None:
+                self.log(f"Skipping surface {indices} due to failed relaxation.")
+                continue
+
+            # Write relaxed POSCAR for surface
+            pos_relaxed_surface = Poscar(relaxed_surface_atoms)
+            pos_relaxed_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}_relaxed.vasp",
+                )
+            )
+
+            # Calculate and log surface energy
+            bulk_energy = self.job_info.get("equilibrium_energy")
+            if final_energy is None or bulk_energy is None:
+                self.log(
+                    f"Skipping surface energy calculation for {self.jid} with indices {indices} due to missing energy values."
+                )
+                continue
+
+            surface_energy = self.calculate_surface_energy(
+                final_energy, bulk_energy, relaxed_surface_atoms, surface
+            )
+
+            # Store the surface energy with the new naming convention
+            surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+            self.job_info[surface_name] = surface_energy
+            self.log(
+                f"Surface energy for {self.jid} with indices {indices}: {surface_energy} J/m^2"
+            )
+
+        # Save updated job info
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log("Surface analysis completed.")
+
+    def relax_surface_structure(self, atoms, indices):
+        """
+        Relax the surface structure and log the process.
+        """
+        filter_type = self.surface_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.surface_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        self.log(f"Starting surface relaxation for {self.jid} with indices {indices}")
+        start_time = time.time()
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+        # Implement other filters if needed
+            pass
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log relaxation results
+        self.log(
+            f"Final energy of FIRE optimization for surface structure: {final_energy}"
+        )
+        self.log(
+            f"Surface relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        end_time = time.time()
+        self.log(
+            f"Surface Relaxation Calculation time: {end_time - start_time} seconds"
+        )
+
+        # Update job info and return relaxed atoms if converged, otherwise return None
+        self.job_info[f"final_energy_surface_{indices}"] = final_energy
+        self.job_info[f"converged_surface_{indices}"] = converged
+
+        # Return both relaxed atoms and the final energy as a tuple
+        return (relaxed_atoms if converged else None), final_energy
+
+    def calculate_surface_energy(
+        self, final_energy, bulk_energy, relaxed_atoms, surface
+    ):
+        """
+        Calculate the surface energy based on the final energy of the relaxed surface and bulk energy.
+        """
+        # Calculate the number of bulk units in the surface supercell
+        num_units = surface.num_atoms / self.atoms.num_atoms
+
+        # Calculate the surface area using the lattice vectors
+        m = relaxed_atoms.lattice.matrix
+        surface_area = np.linalg.norm(np.cross(m[0], m[1]))
+
+        # Calculate surface energy in J/m^2
+        surface_energy = (
+            (final_energy - bulk_energy * num_units) * 16.02176565 / (2 * surface_area)
+        )
+
+        return surface_energy
+
+    def run_phonon3_analysis(self, relaxed_atoms):
+        from phono3py import Phono3py
+        """Run Phono3py analysis, process results, and generate thermal conductivity data."""
+        self.log(f"Starting Phono3py analysis for {self.jid}")
+
+        # Set parameters for the Phono3py calculation
+        dim = self.phonon3_settings.get('dim', [2, 2, 2])
+        distance = self.phonon3_settings.get('distance', 0.2)
+
+        #force_multiplier = 16
+
+        # Convert atoms to Phonopy-compatible object and set up Phono3py
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        bulk = relaxed_atoms.phonopy_converter()
+
+        phonon = Phono3py(bulk, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]])
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for scell in supercells:
+            ase_atoms = AseAtoms(
+                symbols=scell.get_chemical_symbols(),
+                scaled_positions=scell.get_scaled_positions(),
+                cell=scell.get_cell(),
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+            set_of_forces.append(forces)
+
+        # Set the forces and produce third-order force constants
+        forces = np.array(set_of_forces).reshape(-1, len(phonon.supercell), 3)
+        phonon.forces = forces
+        phonon.produce_fc3()
+
+        # Run thermal conductivity calculation
+        phonon.mesh_numbers = 30
+        phonon.init_phph_interaction()
+        phonon.run_thermal_conductivity(
+            temperatures=range(0, 1001, 10), write_kappa=True
+        )
+
+        # Collect thermal conductivity data
+        kappa = phonon.thermal_conductivity.kappa
+        self.log(f"Thermal conductivity: {kappa}")
+
+        # Move generated HDF5 files to the output directory
+        hdf5_file_pattern = "kappa-*.hdf5"
+        for hdf5_file in glob.glob(hdf5_file_pattern):
+            shutil.move(hdf5_file, os.path.join(self.output_dir, hdf5_file))
+
+        # Process Phono3py results and save plots
+        self.process_phonon3_results()
+
+        # Save updated job info to JSON
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(f"Phono3py analysis completed for {self.jid}")
+
+    def process_phonon3_results(self):
+        """Process Phono3py results and generate plots of thermal conductivity."""
+        file_pattern = os.path.join(self.output_dir, "kappa-*.hdf5")
+        file_list = glob.glob(file_pattern)
+
+        temperatures = np.arange(10, 101, 10)
+        kappa_xx_values = []
+
+        if file_list:
+            hdf5_filename = file_list[0]
+            self.log(f"Processing file: {hdf5_filename}")
+
+            for temperature_index in temperatures:
+                converted_kappa = self.convert_kappa_units(
+                    hdf5_filename, temperature_index
+                )
+                kappa_xx = converted_kappa[0]
+                kappa_xx_values.append(kappa_xx)
+                self.log(
+                    f"Temperature index {temperature_index}, converted kappa: {kappa_xx}"
+                )
+
+            # Save results to job_info
+            self.job_info["temperatures"] = temperatures.tolist()
+            self.job_info["kappa_xx_values"] = kappa_xx_values
+
+            # Plot temperature vs. converted kappa (xx element)
+            plt.figure(figsize=(8, 6))
+            plt.plot(
+                temperatures * 10, kappa_xx_values, marker="o", linestyle="-", color="b"
+            )
+            plt.xlabel("Temperature (K)")
+            plt.ylabel("Converted Kappa (xx element)")
+            plt.title("Temperature vs. Converted Kappa (xx element)")
+            plt.grid(True)
+            plt.savefig(
+                os.path.join(self.output_dir, "Temperature_vs_Converted_Kappa.png")
+            )
+            plt.close()
+        else:
+            self.log("No files matching the pattern were found.")
+
+    def convert_kappa_units(self, hdf5_filename, temperature_index):
+        """Convert thermal conductivity kappa from HDF5 file units."""
+        with h5py.File(hdf5_filename, "r") as f:
+            kappa_unit_conversion = f["kappa_unit_conversion"][()]
+            heat_capacity = f["heat_capacity"][:]
+            gv_by_gv = f["gv_by_gv"][:]
+            gamma = f["gamma"][:]
+
+            converted_kappa = (
+                kappa_unit_conversion
+                * heat_capacity[temperature_index, 2, 0]
+                * gv_by_gv[2, 0]
+                / (2 * gamma[temperature_index, 2, 0])
+            )
+
+            return converted_kappa
+
+    def calculate_thermal_expansion(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Calculate the thermal expansion coefficient using QHA."""
+
+        def log(message):
+            with open(self.log_file, "a") as f:
+                f.write(message + "\n")
+            print(message)
+
+        log("Starting thermal expansion analysis...")
+
+        # Step 1: Calculate finer E-V curve
+        vol, y, strained_structures, eos, kv, e0, v0 = self.fine_ev_curve(
+            atoms=relaxed_atoms, dx=np.linspace(-0.05, 0.05, 50)  # Denser grid
+        )
+
+        # Log Bulk modulus, equilibrium energy, and volume
+        log(
+            f"Bulk modulus: {kv} GPa, Equilibrium energy: {y[0]} eV, Volume: {vol[0]} ų"
+        )
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = y[0]
+        self.job_info["equilibrium_volume"] = vol[0]
+
+        # Step 2: Generate phonons for strained structures
+        free_energies, heat_capacities, entropies, temperatures = (
+            self.generate_phonons_for_volumes(
+                strained_structures,
+                calculator=self.calculator,
+                dim=[2, 2, 2],
+                distance=0.2,
+                mesh=[20, 20, 20],
+            )
+        )
+
+        # Step 3: Perform QHA-based thermal expansion analysis
+        alpha = self.perform_qha(
+            volumes=vol,
+            energies=y,
+            free_energies=free_energies,
+            heat_capacities=heat_capacities,
+            entropies=entropies,
+            temperatures=temperatures,
+            output_dir=self.output_dir,
+        )
+
+        self.log(f"Thermal expansion coefficient calculated: {alpha}")
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(
+            f"Thermal expansion analysis information saved to file: {self.jid}_{self.calculator_type}_job_info.json"
+        )
+
+    # Helper Functions Inside the Class
+    def fine_ev_curve(self, atoms, dx=np.linspace(-0.05, 0.05, 50)):
+        """
+        Generate a finer energy-volume curve for strained structures.
+        """
+        y = []
+        vol = []
+        strained_structures = []
+
+        for i in dx:
+            # Apply strain and get strained atoms
+            strained_atoms = atoms.strain_atoms(i)
+            ase_atoms = strained_atoms.ase_converter()  # Convert to ASE Atoms
+            ase_atoms.calc = self.calculator  # Assign the calculator
+
+            # Get potential energy and volume
+            energy = ase_atoms.get_potential_energy()
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+            strained_structures.append(strained_atoms)  # Save the strained structure
+
+        vol = np.array(vol)
+        y = np.array(y)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        eos = EquationOfState(vol, y, eos="murnaghan")
+        v0, e0, B = eos.fit()
+        kv = B / kJ * 1.0e24  # Convert to GPa
+
+        # Log important results
+        self.log(f"Bulk modulus: {kv} GPa")
+        self.log(f"Equilibrium energy: {e0} eV")
+        self.log(f"Equilibrium volume: {v0} ų")
+
+        # Save E-V curve plot
+        fig = plt.figure()
+        eos.plot()
+        ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+        fig.savefig(ev_plot_filename)
+        plt.close(fig)
+        self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+        # Save E-V curve data to a text file
+        ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+        with open(ev_data_filename, "w") as f:
+            f.write("Volume (ų)\tEnergy (eV)\n")
+            for v, e in zip(vol, y):
+                f.write(f"{v}\t{e}\n")
+        self.log(f"E-V curve data saved to {ev_data_filename}")
+
+        # Update job info with the results
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = e0
+        self.job_info["equilibrium_volume"] = v0
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+    def generate_phonons_for_volumes(
+        self, structures, calculator, dim=[2, 2, 2], distance=0.2, mesh=[20, 20, 20]
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        all_free_energies = []
+        all_heat_capacities = []
+        all_entropies = []
+        temperatures = np.arange(0, 300, 6)  # Define temperature range
+
+        for structure in structures:
+            # Convert structure to PhonopyAtoms
+            phonopy_atoms = PhonopyAtoms(
+                symbols=[str(e) for e in structure.elements],
+                positions=structure.cart_coords,
+                cell=structure.lattice.matrix,
+            )
+
+            # Initialize Phonopy object
+            phonon = Phonopy(
+                phonopy_atoms, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]]
+            )
+            phonon.generate_displacements(distance=distance)
+
+            # Calculate forces for displaced structures
+            supercells = phonon.get_supercells_with_displacements()
+            forces = []
+            for scell in supercells:
+                ase_atoms = AseAtoms(
+                    symbols=scell.symbols,
+                    positions=scell.positions,
+                    cell=scell.cell,
+                    pbc=True,
+                )
+                ase_atoms.calc = calculator
+                forces.append(ase_atoms.get_forces())
+
+            phonon.produce_force_constants(forces=forces)
+
+            # Post-processing to get thermal properties
+            phonon.run_mesh(mesh=mesh)
+            phonon.run_thermal_properties(t_min=0, t_step=6, t_max=294)
+            tprop_dict = phonon.get_thermal_properties_dict()
+
+            free_energies = tprop_dict["free_energy"]
+            heat_capacities = tprop_dict["heat_capacity"]
+            entropies = tprop_dict["entropy"]
+
+            all_entropies.append(entropies)
+            all_free_energies.append(free_energies)
+            all_heat_capacities.append(heat_capacities)
+
+        return (
+            np.array(all_free_energies),
+            np.array(all_heat_capacities),
+            np.array(all_entropies),
+            temperatures,
+        )
+
+    def perform_qha(
+        self,
+        volumes,
+        energies,
+        free_energies,
+        heat_capacities,
+        entropies,
+        temperatures,
+        output_dir,
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        # Debugging: print array sizes
+        print(f"Number of temperatures: {len(temperatures)}")
+        print(f"Number of free energy data points: {free_energies.shape}")
+        print(f"Number of volume data points: {len(volumes)}")
+
+        # Ensure that volumes, free_energies, and temperatures are consistent
+        if len(volumes) != len(temperatures):
+            raise ValueError(
+                "The number of volumes must match the number of temperatures"
+            )
+
+        # Initialize the QHA object
+        try:
+            qha = PhonopyQHA(
+                volumes=volumes,
+                electronic_energies=energies,
+                free_energy=free_energies,
+                cv=heat_capacities,
+                entropy=entropies,
+                temperatures=temperatures,
+                eos="murnaghan",  # or another EOS if needed
+                verbose=True,
+            )
+        except IndexError as e:
+            print(f"Error in QHA initialization: {e}")
+            raise
+
+        # Calculate thermal expansion and save plots
+        thermal_expansion_plot = os.path.join(output_dir, "thermal_expansion.png")
+        volume_temperature_plot = os.path.join(output_dir, "volume_temperature.png")
+        helmholtz_volume_plot = os.path.join(output_dir, "helmholtz_volume.png")
+
+        qha.get_thermal_expansion()
+
+        # Save thermal expansion plot
+        qha.plot_thermal_expansion()
+        plt.savefig(thermal_expansion_plot)
+
+        # Save volume-temperature plot
+        qha.plot_volume_temperature()
+        plt.savefig(volume_temperature_plot)
+
+        # Save Helmholtz free energy vs. volume plot
+        qha.plot_helmholtz_volume()
+        plt.savefig(helmholtz_volume_plot)
+
+        # Optionally save thermal expansion coefficient to a file
+        thermal_expansion_file = os.path.join(output_dir, "thermal_expansion.txt")
+        alpha = qha.write_thermal_expansion(filename=thermal_expansion_file)
+
+        return alpha
+
+    def general_melter(self, relaxed_atoms):
+        """Perform MD simulation to melt the structure, then quench it back to room temperature."""
+        self.log(f"Starting MD melting and quenching simulation for {self.jid}")
+
+        calculator = self.setup_calculator()
+        ase_atoms = relaxed_atoms.ase_converter()
+        dim = self.ensure_cell_size(ase_atoms, min_size=self.md_settings.get('min_size', 10.0))
+        supercell = relaxed_atoms.make_supercell_matrix(dim)
+        ase_atoms = supercell.ase_converter()
+        ase_atoms.calc = calculator
+
+        dt = self.md_settings.get('dt', 1) * ase.units.fs
+        temp0 = self.md_settings.get('temp0', 3500)
+        nsteps0 = self.md_settings.get('nsteps0', 1000)
+        temp1 = self.md_settings.get('temp1', 300)
+        nsteps1 = self.md_settings.get('nsteps1', 2000)
+        taut = self.md_settings.get('taut', 20) * ase.units.fs
+        trj = os.path.join(self.output_dir, f"{self.jid}_melt.traj")
+
+        # Initialize velocities and run the first part of the MD simulation
+        from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+        from ase.md.nvtberendsen import NVTBerendsen
+
+        MaxwellBoltzmannDistribution(ase_atoms, temp0 * ase.units.kB)
+        dyn = NVTBerendsen(ase_atoms, dt, temp0, taut=taut, trajectory=trj)
+
+        def myprint():
+            message = f"time={dyn.get_time() / ase.units.fs: 5.0f} fs T={ase_atoms.get_temperature(): 3.0f} K"
+            self.log(message)
+
+        dyn.attach(myprint, interval=20)
+        dyn.run(nsteps0)
+
+        # Cool down to room temperature
+        dyn.set_temperature(temp1)
+        dyn.run(nsteps1)
+
+        # Convert back to JARVIS atoms and save the final structure
+        final_atoms = ase_to_atoms(ase_atoms)
+        poscar_filename = os.path.join(
+            self.output_dir, f"POSCAR_{self.jid}_quenched_{self.calculator_type}.vasp"
+        )
+        from ase.io import write
+
+        write(poscar_filename, final_atoms.ase_converter(), format="vasp")
+        self.log(f"MD simulation completed. Final structure saved to {poscar_filename}")
+        self.job_info["quenched_atoms"] = final_atoms.to_dict()
+
+        return final_atoms
+
+    def calculate_rdf(self, quenched_atoms):
+        """Calculate Radial Distribution Function (RDF) for quenched structure and save plot."""
+        self.log(f"Starting RDF calculation for {self.jid}")
+        ase_atoms = quenched_atoms.ase_converter()
+        rmax = 3.5
+        nbins = 200
+
+        def perform_rdf_calculation(rmax):
+            from ase.ga.utilities import get_rdf
+
+            rdfs, distances = get_rdf(ase_atoms, rmax, nbins)
+            plt.figure()
+            plt.plot(distances, rdfs)
+            plt.xlabel("Distance (Å)")
+            plt.ylabel("RDF")
+            plt.title(
+                f"Radial Distribution Function for {self.jid} ({self.calculator_type})"
+            )
+            rdf_plot_filename = os.path.join(
+                self.output_dir, f"RDF_{self.jid}_{self.calculator_type}.png"
+            )
+            plt.savefig(rdf_plot_filename)
+            plt.close()
+            self.job_info["rdf_plot"] = rdf_plot_filename
+            self.log(f"RDF plot saved to {rdf_plot_filename}")
+            return rdf_plot_filename
+
+        try:
+            perform_rdf_calculation(rmax)
+        except ValueError as e:
+            if "The cell is not large enough" in str(e):
+                recommended_rmax = float(str(e).split("<")[1].split("=")[1])
+                self.log(f"Error: {e}. Adjusting rmax to {recommended_rmax}.")
+                perform_rdf_calculation(recommended_rmax)
+            else:
+                self.log(f"Error: {e}")
+                raise
+
+    def ensure_cell_size(self, ase_atoms, min_size):
+        """Ensure that all cell dimensions are at least min_size."""
+        cell_lengths = ase_atoms.get_cell().lengths()
+        scale_factors = np.ceil(min_size / cell_lengths).astype(int)
+        supercell_dims = [max(1, scale) for scale in scale_factors]
+        return supercell_dims
+
+    def analyze_interfaces(self):
+        """Perform interface analysis using intermat package."""
+        if not self.film_jid or not self.substrate_jid:
+            self.log("Film JID or substrate JID not provided, skipping interface analysis.")
+            return
+
+        self.log(f"Starting interface analysis between {self.film_jid} and {self.substrate_jid}")
+
+        # Ensure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # Prepare config
+        config = {
+            "film_jid": self.film_jid,
+            "substrate_jid": self.substrate_jid,
+            "film_index": self.film_index,
+            "substrate_index": self.substrate_index,
+            "disp_intvl": 0.05,
+            "calculator_method": self.calculator_type.lower(),
+        }
+
+        config_filename = os.path.join(
+            self.output_dir,
+            f"config_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.json"
+        )
+
+        # Save config file
+        save_dict_to_json(config, config_filename)
+        self.log(f"Config file created: {config_filename}")
+
+        # Run intermat script using subprocess in self.output_dir
+        command = f"run_intermat.py --config_file {os.path.basename(config_filename)}"
+        self.log(f"Running command: {command}")
+
+        try:
+            result = subprocess.run(
+                command,
+                shell=True,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=self.output_dir  # Set the working directory for the subprocess
+            )
+            self.log(f"Command output: {result.stdout}")
+        except subprocess.CalledProcessError as e:
+            self.log(f"Command failed with error: {e.stderr}")
+            return
+
+        # After execution, check for outputs in self.output_dir
+        main_results_filename = os.path.join(self.output_dir, "intermat_results.json")
+        if not os.path.exists(main_results_filename):
+            self.log(f"Results file not found: {main_results_filename}")
+            return
+
+        res = load_dict_from_json(main_results_filename)
+        w_adhesion = res.get("wads", [])
+        systems_info = res.get("systems", {})
+
+        # Handle intmat.png
+        intmat_filename = os.path.join(self.output_dir, "intmat.png")
+        if os.path.exists(intmat_filename):
+            new_intmat_filename = os.path.join(
+                self.output_dir,
+                f"intmat_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.png"
+            )
+            os.rename(intmat_filename, new_intmat_filename)
+            self.job_info["intmat_plot"] = new_intmat_filename
+            self.log(f"intmat.png saved as {new_intmat_filename}")
+        else:
+            self.log("intmat.png not found.")
+
+        if "wads" in res:
+            # Save additional plots or data as needed
+            self.job_info["interface_scan_results"] = main_results_filename
+            self.job_info["w_adhesion"] = w_adhesion
+            self.job_info["systems_info"] = systems_info
+            self.log(f"Interface scan results saved to {main_results_filename}")
+            self.log(f"w_adhesion: {w_adhesion}")
+            self.log(f"systems_info: {systems_info}")
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+        else:
+            self.log(f"No 'wads' key in results file: {main_results_filename}")
+
+    def get_job_info_filename(self):
+        if hasattr(self, 'jid') and self.jid:
+            return os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            )
+        else:
+            return os.path.join(
+                self.output_dir, f"Interface_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}_job_info.json"
+            )
+
+    import numpy as np
+    import pandas as pd
+    from sklearn.metrics import mean_absolute_error, r2_score
+    import matplotlib.pyplot as plt
+    from matplotlib.gridspec import GridSpec
+    import os
+
+    def run_all(self):
+        """Run selected analyses based on configuration."""
+        # Start timing the entire run
+        start_time = time.time()
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for analysis.")
+            self.atoms = self.atoms.get_conventional_atoms
+        else:
+            self.atoms = self.atoms
+        # Relax the structure if specified
+        if 'relax_structure' in self.properties_to_calculate:
+            relaxed_atoms = self.relax_structure()
+        else:
+            relaxed_atoms = self.atoms
+
+        # Proceed only if the structure is relaxed or original atoms are used
+        if relaxed_atoms is None:
+            self.log("Relaxation did not converge. Exiting.")
+            return
+
+        if 'calculate_forces' in self.properties_to_calculate:
+            self.calculate_forces(self.atoms)
+
+        # Lattice parameters before and after relaxation
+        lattice_initial = self.atoms.lattice
+        lattice_final = relaxed_atoms.lattice
+
+        # Prepare final results dictionary
+        final_results = {}
+
+        # Initialize variables for error calculation
+        err_a = err_b = err_c = err_vol = err_form = err_kv = err_c11 = err_c44 = err_surf_en = err_vac_en = np.nan
+        form_en_entry = kv_entry = c11_entry = c44_entry = 0
+
+        # Calculate E-V curve and bulk modulus if specified
+        if 'calculate_ev_curve' in self.properties_to_calculate:
+            _, _, _, _, bulk_modulus, _, _ = self.calculate_ev_curve(relaxed_atoms)
+            kv_entry = self.reference_data.get("bulk_modulus_kv", 0)
+            final_results['modulus'] = {
+                'kv': bulk_modulus,
+                'kv_entry': kv_entry
+            }
+            err_kv = mean_absolute_error([kv_entry], [bulk_modulus]) if bulk_modulus is not None else np.nan
+
+                
+        # Formation energy
+        if 'calculate_formation_energy' in self.properties_to_calculate:
+            formation_energy = self.calculate_formation_energy(relaxed_atoms)
+            form_en_entry = self.reference_data.get("formation_energy_peratom", 0)
+            final_results['form_en'] = {
+                'form_energy': formation_energy,
+                'form_energy_entry': form_en_entry
+            }
+            err_form = mean_absolute_error([form_en_entry], [formation_energy])
+
+        # Elastic tensor
+        if 'calculate_elastic_tensor' in self.properties_to_calculate:
+            elastic_tensor = self.calculate_elastic_tensor(relaxed_atoms)
+            c11_entry = self.reference_data.get("elastic_tensor", [[0]])[0][0]
+            c44_entry = self.reference_data.get("elastic_tensor", [[0, 0, 0, [0, 0, 0, 0]]])[3][3]
+            final_results['elastic_tensor'] = {
+                'c11': elastic_tensor.get("C_11", 0),
+                'c44': elastic_tensor.get("C_44", 0),
+                'c11_entry': c11_entry,
+                'c44_entry': c44_entry
+            }
+            err_c11 = mean_absolute_error([c11_entry], [elastic_tensor.get("C_11", np.nan)])
+            err_c44 = mean_absolute_error([c44_entry], [elastic_tensor.get("C_44", np.nan)])
+
+        # Phonon analysis
+        if 'run_phonon_analysis' in self.properties_to_calculate:
+            phonon, zpe = self.run_phonon_analysis(relaxed_atoms)
+            final_results['zpe'] = zpe
+        else:
+            zpe = None
+
+        # Surface energy analysis
+        if 'analyze_surfaces' in self.properties_to_calculate:
+            self.analyze_surfaces()
+            surf_en, surf_en_entry = [], []
+            surface_entries = get_surface_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+
+            indices_list = self.surface_settings.get('indices_list', [
+                [1, 0, 0],
+                [1, 1, 1],
+                [1, 1, 0],
+                [0, 1, 1],
+                [0, 0, 1],
+                [0, 1, 0],
+            ])
+
+            for indices in indices_list:
+                surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+                calculated_surface_energy = self.job_info.get(surface_name, 0)
+                try:
+                    # Try to match the surface entry
+                    matching_entry = next(
+                        (entry for entry in surface_entries if entry['name'].strip() == surface_name.strip()),
+                        None
+                    )
+                    if matching_entry and calculated_surface_energy != 0 and matching_entry["surf_en_entry"] != 0:
+                        surf_en.append(calculated_surface_energy)
+                        surf_en_entry.append(matching_entry["surf_en_entry"])
+                    else:
+                        print(f"No valid matching entry found for {surface_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing surface {surface_name}: {e}")
+                    self.log(f"Error processing surface {surface_name}: {str(e)}")
+                    continue  # Skip this surface and move to the next one
+            final_results['surface_energy'] = [
+                {
+                    "name": f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}",
+                    "surf_en": se,
+                    "surf_en_entry": see
+                }
+                for se, see, indices in zip(surf_en, surf_en_entry, indices_list)
+            ]
+            err_surf_en = mean_absolute_error(surf_en_entry, surf_en) if surf_en else np.nan
+
+        # Vacancy energy analysis
+        if 'analyze_defects' in self.properties_to_calculate:
+            self.analyze_defects()
+            vac_en, vac_en_entry = [], []
+            vacancy_entries = get_vacancy_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+            for defect in Vacancy(self.atoms).generate_defects(on_conventional_cell=True, enforce_c_size=8, extend=1):
+                defect_name = f"{self.jid}_{defect.to_dict()['symbol']}"
+                vacancy_energy = self.job_info.get(f"vacancy_formation_energy for {defect_name}", 0)
+                try:
+                    # Try to match the vacancy entry
+                    matching_entry = next(
+                        (entry for entry in vacancy_entries if entry['symbol'] == defect_name),
+                        None
+                    )
+                    if matching_entry and vacancy_energy != 0 and matching_entry['vac_en_entry'] != 0:
+                        vac_en.append(vacancy_energy)
+                        vac_en_entry.append(matching_entry['vac_en_entry'])
+                    else:
+                        print(f"No valid matching entry found for {defect_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing defect {defect_name}: {e}")
+                    self.log(f"Error processing defect {defect_name}: {str(e)}")
+                    continue  # Skip this defect and move to the next one
+            final_results['vacancy_energy'] = [
+                {"name": ve_name, "vac_en": ve, "vac_en_entry": vee}
+                for ve_name, ve, vee in zip(
+                    [f"{self.jid}_{defect.to_dict()['symbol']}" for defect in Vacancy(self.atoms).generate_defects(
+                        on_conventional_cell=True, enforce_c_size=8, extend=1
+                    )],
+                    vac_en,
+                    vac_en_entry
+                )
+            ]
+            err_vac_en = mean_absolute_error(vac_en_entry, vac_en) if vac_en else np.nan
+
+        # Additional analyses
+        if 'analyze_interfaces' in self.properties_to_calculate and self.film_jid and self.substrate_jid:
+            self.analyze_interfaces()
+
+        if 'run_phonon3_analysis' in self.properties_to_calculate:
+            self.run_phonon3_analysis(relaxed_atoms)
+
+        if 'calculate_thermal_expansion' in self.properties_to_calculate:
+            self.calculate_thermal_expansion(relaxed_atoms)
+
+        if 'general_melter' in self.properties_to_calculate:
+            quenched_atoms = self.general_melter(relaxed_atoms)
+            if 'calculate_rdf' in self.properties_to_calculate:
+                self.calculate_rdf(quenched_atoms)
+
+        # Record lattice parameters
+        final_results['energy'] = {
+            "initial_a": lattice_initial.a,
+            "initial_b": lattice_initial.b,
+            "initial_c": lattice_initial.c,
+            "initial_vol": lattice_initial.volume,
+            "final_a": lattice_final.a,
+            "final_b": lattice_final.b,
+            "final_c": lattice_final.c,
+            "final_vol": lattice_final.volume,
+            "energy": self.job_info.get("final_energy_structure", 0)
+        }
+
+        # Error calculations
+        err_a = mean_absolute_error([lattice_initial.a], [lattice_final.a])
+        err_b = mean_absolute_error([lattice_initial.b], [lattice_final.b])
+        err_c = mean_absolute_error([lattice_initial.c], [lattice_final.c])
+        err_vol = mean_absolute_error([lattice_initial.volume], [lattice_final.volume])
+
+        # Create an error dictionary
+        error_dat = {
+            "err_a": err_a,
+            "err_b": err_b,
+            "err_c": err_c,
+            "err_form": err_form,
+            "err_vol": err_vol,
+            "err_kv": err_kv,
+            "err_c11": err_c11,
+            "err_c44": err_c44,
+            "err_surf_en": err_surf_en,
+            "err_vac_en": err_vac_en,
+            "time": time.time() - start_time
+        }
+
+        print("Error metrics calculated:", error_dat)
+
+        # Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+        # Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+        # Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        # Write results to a JSON file
+        output_file = os.path.join(self.output_dir, f"{self.jid}_{self.calculator_type}_results.json")
+        save_dict_to_json(final_results, output_file)
+
+        # Log total time
+        total_time = error_dat['time']
+        self.log(f"Total time for run: {total_time} seconds")
+
+        return error_dat
+
+
+# Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+# Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+# Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        return error_dat
+
+    def plot_error_scorecard(self, df):
+        import plotly.express as px
+
+        fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+        unique_dir = os.path.basename(self.output_dir)
+        fname_plot = os.path.join(self.output_dir, f"{unique_dir}_error_scorecard.png")
+        fig.write_image(fname_plot)
+        fig.show()
+
+def analyze_multiple_structures(jid_list, calculator_types, chemical_potentials_file):
+    composite_error_data = {}
+
+    for calculator_type in calculator_types:
+        # List to store individual error DataFrames
+        error_dfs = []
+
+        for jid in jid_list:
+            print(f"Analyzing {jid} with {calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                jid=jid,
+                calculator_type=calculator_type,
+                chemical_potentials_file=chemical_potentials_file,
+            )
+            # Run analysis and get error data
+            error_dat = analyzer.run_all()
+            error_df = pd.DataFrame([error_dat])
+            error_dfs.append(error_df)
+
+        # Concatenate all error DataFrames
+        all_errors_df = pd.concat(error_dfs, ignore_index=True)
+
+        # Compute composite errors by ignoring NaN values
+        composite_error = all_errors_df.mean(skipna=True).to_dict()
+
+        # Store the composite error data for this calculator type
+        composite_error_data[calculator_type] = composite_error
+
+    # Once all materials and calculators have been processed, create a DataFrame
+    composite_df = pd.DataFrame(composite_error_data).transpose()
+
+    # Plot the composite scorecard
+    plot_composite_scorecard(composite_df)
+
+    # Save the composite dataframe
+    composite_df.to_csv("composite_error_data.csv", index=True)
+
+def analyze_multiple_interfaces(film_jid_list, substrate_jid_list, calculator_types, chemical_potentials_file, film_index="1_1_0", substrate_index="1_1_0"):
+    for calculator_type in calculator_types:
+        for film_jid in film_jid_list:
+            for substrate_jid in substrate_jid_list:
+                print(f"Analyzing interface between {film_jid} and {substrate_jid} with {calculator_type}...")
+                analyzer = MaterialsAnalyzer(
+                    calculator_type=calculator_type,
+                    chemical_potentials_file=chemical_potentials_file,
+                    film_jid=film_jid,
+                    substrate_jid=substrate_jid,
+                    film_index=film_index,
+                    substrate_index=substrate_index,
+                )
+                analyzer.analyze_interfaces()
+
+def plot_composite_scorecard(df):
+    """Plot the composite scorecard for all calculators"""
+    fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+    fig.update_layout(title="Composite Scorecard for Calculators")
+    
+    # Save plot
+    fname_plot = "composite_error_scorecard.png"
+    fig.write_image(fname_plot)
+    fig.show()
+
+
+class MLearnForcesAnalyzer:
+    def __init__(self, calculator_type, mlearn_elements, output_dir=None):
+        self.calculator_type = calculator_type
+        self.mlearn_elements = mlearn_elements
+        elements_str = "_".join(self.mlearn_elements)
+        self.output_dir = output_dir or f"mlearn_analysis_{elements_str}_{calculator_type}"
+        os.makedirs(self.output_dir, exist_ok=True)
+        self.log_file = os.path.join(self.output_dir, "mlearn_analysis_log.txt")
+        self.setup_logger()
+        self.calculator = self.setup_calculator()
+        self.job_info = {
+            "calculator_type": calculator_type,
+            "mlearn_elements": mlearn_elements,
+        }
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger("MLearnForcesAnalyzer")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        self.logger.info(message)
+        print(message)
+
+    def setup_calculator(self):
+        return setup_calculator(self.calculator_type)
+
+    def run(self):
+        for element in self.mlearn_elements:
+            self.compare_mlearn_properties(element)
+
+    def compare_mlearn_properties(self, element):
+        """
+        Compare forces and stresses calculated by the FF calculator with mlearn DFT data for a given element.
+
+        Args:
+            element (str): Element symbol to filter structures (e.g., 'Si').
+        """
+        # Download the mlearn dataset if not already present
+        mlearn_zip_path = 'mlearn.json.zip'
+        if not os.path.isfile(mlearn_zip_path):
+            self.log("Downloading mlearn dataset...")
+            url = 'https://figshare.com/ndownloader/files/40357663'
+            response = requests.get(url)
+            with open(mlearn_zip_path, 'wb') as f:
+                f.write(response.content)
+            self.log("Download completed.")
+
+        # Read the JSON data from the zip file
+        with zipfile.ZipFile(mlearn_zip_path, 'r') as z:
+            with z.open('mlearn.json') as f:
+                mlearn_data = json.load(f)
+
+        # Convert mlearn data to DataFrame
+        df = pd.DataFrame(mlearn_data)
+
+        # Filter the dataset for the specified element
+        df['elements'] = df['atoms'].apply(lambda x: x['elements'])
+        df = df[df['elements'].apply(lambda x: element in x)]
+        df = df.reset_index(drop=True)
+        self.log(f"Filtered dataset to {len(df)} entries containing element '{element}'")
+
+        # Initialize lists to store results
+        force_results = []
+        stress_results = []
+
+        # Iterate over each structure
+        for idx, row in df.iterrows():
+            jid = row.get('jid', f"structure_{idx}")
+            atoms_dict = row['atoms']
+            atoms = Atoms.from_dict(atoms_dict)
+            dft_forces = np.array(row['forces'])
+            dft_stresses = np.array(row['stresses'])  # Original stresses in kBar
+
+            # Convert DFT stresses from kBar to GPa
+            dft_stresses_GPa = dft_stresses * -0.1  # kBar to GPa
+
+            # Convert DFT stresses to full 3x3 tensors
+            if dft_stresses_GPa.ndim == 1 and dft_stresses_GPa.size == 6:
+                dft_stress_tensor = voigt_6_to_full_3x3_stress(dft_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: DFT stresses not in expected format.")
+                continue  # Skip structures with unexpected stress format
+
+            # Calculate predicted properties
+            predicted_forces, predicted_stresses = self.calculate_properties(atoms)
+
+            # Convert predicted stresses from eV/ų to GPa
+            if predicted_stresses is not None and predicted_stresses.size == 6:
+                predicted_stresses_GPa = predicted_stresses * 160.21766208  # eV/ų to GPa
+                predicted_stress_tensor = voigt_6_to_full_3x3_stress(predicted_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: Predicted stresses not available.")
+                continue  # Skip structures where stresses are not available
+
+            # Flatten the 3x3 stress tensors to 9-component arrays for comparison
+            dft_stress_flat = dft_stress_tensor.flatten()
+            predicted_stress_flat = predicted_stress_tensor.flatten()
+
+            # Store the results
+            force_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_forces.flatten())),
+                'prediction': ';'.join(map(str, predicted_forces.flatten())),
+            })
+            stress_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_stress_flat)),
+                'prediction': ';'.join(map(str, predicted_stress_flat)),
+            })
+
+            # Optional: Progress indicator
+            if idx % 10 == 0:
+                self.log(f"Processed {idx + 1}/{len(df)} structures.")
+
+        # Ensure we have data to process
+        if not force_results or not stress_results:
+            self.log("No valid data found for forces or stresses. Exiting.")
+            return
+
+        # Save results to CSV files
+        force_df = pd.DataFrame(force_results)
+        force_csv = os.path.join(self.output_dir, f'AI-MLFF-forces-mlearn_{element}-test-multimae.csv')
+        force_df.to_csv(force_csv, index=False)
+        self.log(f"Saved force comparison data to '{force_csv}'")
+
+        stress_df = pd.DataFrame(stress_results)
+        stress_csv = os.path.join(self.output_dir, f'AI-MLFF-stresses-mlearn_{element}-test-multimae.csv')
+        stress_df.to_csv(stress_csv, index=False)
+        self.log(f"Saved stress comparison data to '{stress_csv}'")
+
+        # Zip the CSV files
+        self.zip_file(force_csv)
+        self.zip_file(stress_csv)
+
+        # Calculate error metrics
+        # Forces MAE
+        target_forces = np.concatenate(force_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_forces = np.concatenate(force_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        forces_mae = mean_absolute_error(target_forces, pred_forces)
+        self.log(f"Forces MAE for element '{element}': {forces_mae:.6f} eV/Å")
+
+        # Stresses MAE
+        target_stresses = np.concatenate(stress_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_stresses = np.concatenate(stress_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        stresses_mae = mean_absolute_error(target_stresses, pred_stresses)
+        self.log(f"Stresses MAE for element '{element}': {stresses_mae:.6f} GPa")
+
+        # Save MAE to job_info
+        self.job_info[f'forces_mae_{element}'] = forces_mae
+        self.job_info[f'stresses_mae_{element}'] = stresses_mae
+        self.save_job_info()
+
+        # Plot parity plots
+        forces_plot_filename = os.path.join(self.output_dir, f'forces_parity_plot_{element}.png')
+        self.plot_parity(target_forces, pred_forces, 'Forces', 'eV/Å', forces_plot_filename, element)
+
+        stresses_plot_filename = os.path.join(self.output_dir, f'stresses_parity_plot_{element}.png')
+        self.plot_parity(target_stresses, pred_stresses, 'Stresses', 'GPa', stresses_plot_filename, element)
+
+    def calculate_properties(self, atoms):
+        """
+        Calculate forces and stresses on the given atoms.
+
+        Returns:
+            Tuple of forces and stresses.
+        """
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Calculate properties
+        forces = ase_atoms.get_forces()
+        stresses = ase_atoms.get_stress()  # Voigt 6-component stress
+
+        return forces, stresses  # Return forces and stresses in Voigt notation
+
+    def plot_parity(self, target, prediction, property_name, units, filename, element):
+        """
+        Plot parity plot for a given property.
+
+        Args:
+            target (array-like): Target values.
+            prediction (array-like): Predicted values.
+            property_name (str): Name of the property (e.g., 'Forces').
+            units (str): Units of the property (e.g., 'eV/Å' or 'GPa').
+            filename (str): Filename to save the plot.
+            element (str): Element symbol.
+        """
+        plt.figure(figsize=(8, 8), dpi=300)
+        plt.scatter(target, prediction, alpha=0.5, edgecolors='k', s=20)
+        min_val = min(np.min(target), np.min(prediction))
+        max_val = max(np.max(target), np.max(prediction))
+        plt.plot([min_val, max_val], [min_val, max_val], 'k--', lw=2)
+        plt.xlabel(f'Target {property_name} ({units})', fontsize=14)
+        plt.ylabel(f'Predicted {property_name} ({units})', fontsize=14)
+        plt.title(f'Parity Plot for {property_name} - Element {element}', fontsize=16)
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(filename)
+        plt.close()
+        self.log(f"Saved parity plot for {property_name} as '{filename}'")
+
+    def zip_file(self, filename):
+        zip_filename = filename + '.zip'
+        with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
+            zf.write(filename, arcname=os.path.basename(filename))
+        os.remove(filename)  # Remove the original file
+        self.log(f"Zipped data to '{zip_filename}'")
+
+    def save_job_info(self):
+        job_info_filename = os.path.join(self.output_dir, f"mlearn_{self.calculator_type}_job_info.json")
+        with open(job_info_filename, 'w') as f:
+            json.dump(self.job_info, f, indent=4)
+
+
+#jid_list=['JVASP-1002']
+jid_list_all = [ 'JVASP-1002', 'JVASP-816', 'JVASP-867', 'JVASP-1029', 'JVASP-861','JVASP-30', 'JVASP-8169', 'JVASP-890', 'JVASP-8158','JVASP-8118',
+    'JVASP-107', 'JVASP-39', 'JVASP-7844', 'JVASP-35106', 'JVASP-1174',
+    'JVASP-1372', 'JVASP-91', 'JVASP-1186', 'JVASP-1408', 'JVASP-105410',
+    'JVASP-1177', 'JVASP-79204', 'JVASP-1393', 'JVASP-1312', 'JVASP-1327',
+    'JVASP-1183', 'JVASP-1192', 'JVASP-8003', 'JVASP-96', 'JVASP-1198',
+    'JVASP-1195', 'JVASP-9147', 'JVASP-41', 'JVASP-34674', 'JVASP-113',
+    'JVASP-32', 'JVASP-840', 'JVASP-21195', 'JVASP-981', 'JVASP-969',
+    'JVASP-802', 'JVASP-943', 'JVASP-14812', 'JVASP-984', 'JVASP-972',
+    'JVASP-958', 'JVASP-901', 'JVASP-1702', 'JVASP-931', 'JVASP-963',
+    'JVASP-95', 'JVASP-1201', 'JVASP-14837', 'JVASP-825', 'JVASP-966',
+    'JVASP-993', 'JVASP-23', 'JVASP-828', 'JVASP-1189', 'JVASP-810',
+    'JVASP-7630', 'JVASP-819', 'JVASP-1180', 'JVASP-837', 'JVASP-919',
+    'JVASP-7762', 'JVASP-934', 'JVASP-858', 'JVASP-895']
+#calculator_types = ["alignn_ff_aff307k_lmdb_param_low_rad_use_force_mult_mp_tak4","alignn_ff_v5.27.2024","alignn_ff_aff307k_kNN_2_2_128"]
+
+if __name__ == "__main__":
+    import pprint
+    parser = argparse.ArgumentParser(description="Run Materials Analyzer")
+    parser.add_argument("--input_file", default="input.json", type=str, help="Path to the input configuration JSON file")
+    args = parser.parse_args()
+
+    input_file = loadjson(args.input_file)
+    input_file_data = CHIPSFFConfig(**input_file)
+    pprint.pprint(input_file_data.dict())
+
+    # If film_id is provided, treat it as a list
+    film_jids = input_file_data.film_id if input_file_data.film_id else []
+
+    # If substrate_id is provided, treat it as a list
+    substrate_jids = input_file_data.substrate_id if input_file_data.substrate_id else []
+
+    # Case 1: Interface calculations with film_jid and substrate_jid
+    if film_jids and substrate_jids:
+        # Loop through all film and substrate JIDs and perform interface analysis
+        for film_jid, substrate_jid in zip(film_jids, substrate_jids):
+            print(f"Analyzing interface between {film_jid} and {substrate_jid} with {input_file_data.calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                calculator_type=input_file_data.calculator_type,
+                chemical_potentials_file=input_file_data.chemical_potentials_file,
+                film_jid=film_jid,
+                substrate_jid=substrate_jid,
+                film_index=input_file_data.film_index,
+                substrate_index=input_file_data.substrate_index,
+                bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+                phonon_settings=input_file_data.phonon_settings,
+                properties_to_calculate=input_file_data.properties_to_calculate,
+            )
+            analyzer.analyze_interfaces()
+
+    # Case 2: Single JID provided
+    elif input_file_data.jid and input_file_data.calculator_type:
+        print(f"Analyzing {input_file_data.jid} with {input_file_data.calculator_type}...")
+        analyzer = MaterialsAnalyzer(
+            jid=input_file_data.jid,
+            calculator_type=input_file_data.calculator_type,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+        analyzer.run_all()
+
+    # Case 3: Multiple JIDs and calculator types provided (batch processing)
+    elif input_file_data.jid_list and input_file_data.calculator_types:
+        analyze_multiple_structures(
+            jid_list=input_file_data.jid_list,
+            calculator_types=input_file_data.calculator_types,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+
+    elif input_file_data.mlearn_elements and input_file_data.calculator_type:
+        print(f"Running mlearn forces comparison for elements {input_file_data.mlearn_elements} with {input_file_data.calculator_type}...")
+        mlearn_analyzer = MLearnForcesAnalyzer(
+            calculator_type=input_file_data.calculator_type,
+            mlearn_elements=input_file_data.mlearn_elements
+        )
+        mlearn_analyzer.run()
+    else:
+        print("Please provide valid arguments in the configuration file.")
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/AI-MLFF-forces-mlearnall_Cu-test-multimae.csv.zip b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Cu-test-multimae.csv.zip
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/AI-MLFF-forces-mlearnall_Cu-test-multimae.csv.zip
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Cu-test-multimae.csv.zip
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/metadata.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/metadata.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/metadata.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/mlearn_alignn_ff_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/mlearn_alignn_ff_job_info.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/mlearn_analysis_log.txt
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/mlearn_analysis_log.txt
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/mlearn_analysis_log.txt
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/run.sh
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/run.sh
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/run.sh
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/run_chipsff.py
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff/run_chipsff.py
rename to jarvis_leaderboard/contributions/mlearn_analysis_Cu_alignn_ff_5_27_24/run_chipsff.py
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diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/metadata.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/metadata.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/metadata.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
new file mode 100644
index 000000000..eed4085da
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
@@ -0,0 +1,8 @@
+{
+    "calculator_type": "alignn_ff_12_2_24",
+    "mlearn_elements": [
+        "Ge"
+    ],
+    "forces_mae_Ge": 0.3953967136776566,
+    "stresses_mae_Ge": 0.780313668468333
+}
\ No newline at end of file
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/mlearn_analysis_log.txt
new file mode 100644
index 000000000..d966bc40f
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/mlearn_analysis_log.txt
@@ -0,0 +1,35 @@
+2024-12-03 16:35:00,317 - INFO - Filtered dataset to 253 entries containing element 'Ge'
+2024-12-03 16:35:06,862 - INFO - Processed 1/253 structures.
+2024-12-03 16:35:07,930 - INFO - Processed 11/253 structures.
+2024-12-03 16:35:08,922 - INFO - Processed 21/253 structures.
+2024-12-03 16:35:09,918 - INFO - Processed 31/253 structures.
+2024-12-03 16:35:11,485 - INFO - Processed 41/253 structures.
+2024-12-03 16:35:13,671 - INFO - Processed 51/253 structures.
+2024-12-03 16:35:15,302 - INFO - Processed 61/253 structures.
+2024-12-03 16:35:16,906 - INFO - Processed 71/253 structures.
+2024-12-03 16:35:18,436 - INFO - Processed 81/253 structures.
+2024-12-03 16:35:20,031 - INFO - Processed 91/253 structures.
+2024-12-03 16:35:21,609 - INFO - Processed 101/253 structures.
+2024-12-03 16:35:23,173 - INFO - Processed 111/253 structures.
+2024-12-03 16:35:24,746 - INFO - Processed 121/253 structures.
+2024-12-03 16:35:26,216 - INFO - Processed 131/253 structures.
+2024-12-03 16:35:27,865 - INFO - Processed 141/253 structures.
+2024-12-03 16:35:29,462 - INFO - Processed 151/253 structures.
+2024-12-03 16:35:31,006 - INFO - Processed 161/253 structures.
+2024-12-03 16:35:31,747 - INFO - Processed 171/253 structures.
+2024-12-03 16:35:32,898 - INFO - Processed 181/253 structures.
+2024-12-03 16:35:34,102 - INFO - Processed 191/253 structures.
+2024-12-03 16:35:35,310 - INFO - Processed 201/253 structures.
+2024-12-03 16:35:36,190 - INFO - Processed 211/253 structures.
+2024-12-03 16:35:37,106 - INFO - Processed 221/253 structures.
+2024-12-03 16:35:38,029 - INFO - Processed 231/253 structures.
+2024-12-03 16:35:39,439 - INFO - Processed 241/253 structures.
+2024-12-03 16:35:41,176 - INFO - Processed 251/253 structures.
+2024-12-03 16:35:41,413 - INFO - Saved force comparison data to 'mlearn_analysis_Ge_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Ge-test-multimae.csv'
+2024-12-03 16:35:41,420 - INFO - Saved stress comparison data to 'mlearn_analysis_Ge_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Ge-test-multimae.csv'
+2024-12-03 16:35:41,548 - INFO - Zipped data to 'mlearn_analysis_Ge_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Ge-test-multimae.csv.zip'
+2024-12-03 16:35:41,559 - INFO - Zipped data to 'mlearn_analysis_Ge_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Ge-test-multimae.csv.zip'
+2024-12-03 16:35:41,586 - INFO - Forces MAE for element 'Ge': 0.395397 eV/Å
+2024-12-03 16:35:41,591 - INFO - Stresses MAE for element 'Ge': 0.780314 GPa
+2024-12-03 16:35:44,299 - INFO - Saved parity plot for Forces as 'mlearn_analysis_Ge_alignn_ff_12_2_24/forces_parity_plot_Ge.png'
+2024-12-03 16:35:45,337 - INFO - Saved parity plot for Stresses as 'mlearn_analysis_Ge_alignn_ff_12_2_24/stresses_parity_plot_Ge.png'
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/run.sh
new file mode 100644
index 000000000..0e331ca03
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/run.sh
@@ -0,0 +1,148 @@
+#!/bin/bash
+
+# Create logs directory if it doesn't exist
+mkdir -p logs
+
+# Define arrays of JIDs, calculators, and mlearn elements
+calculator_types=("alignn_ff_12_2_24")
+mlearn_elements=("Si" "Ge" "Cu" "Ni" "Li" "Mo")
+
+# Function to submit a job for bulk material calculations
+submit_bulk_job() {
+  local jid=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=30-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=${jid}_${calculator}
+#SBATCH --output=logs/${jid}_${calculator}_%j.out
+#SBATCH --error=logs/${jid}_${calculator}_%j.err
+
+# Generate input JSON file for this combination
+cat > input_${jid}_${calculator}.json <<JSON
+{
+  "jid": "$jid",
+  "calculator_type": "$calculator",
+  "chemical_potentials_file": "chemical_potentials.json",
+  "properties_to_calculate": [
+    "relax_structure",
+    "calculate_ev_curve",
+    "calculate_formation_energy",
+    "calculate_elastic_tensor",
+    "run_phonon_analysis",
+    "analyze_surfaces",
+    "analyze_defects",
+    "run_phonon3_analysis",
+    "general_melter",
+    "calculate_rdf"
+  ],
+  "bulk_relaxation_settings": {
+    "filter_type": "ExpCellFilter",
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": false
+    }
+  },
+  "phonon_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "use_conventional_cell": true,
+  "surface_settings": {
+    "indices_list": [
+      [0, 1, 0],
+      [0, 0, 1]
+    ],
+    "layers": 4,
+    "vacuum": 18,
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "defect_settings": {
+    "generate_settings": {
+      "on_conventional_cell": true,
+      "enforce_c_size": 8,
+      "extend": 1
+    },
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "phonon3_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "md_settings": {
+    "dt": 1,
+    "temp0": 35,
+    "nsteps0": 10,
+    "temp1": 200,
+    "nsteps1": 20,
+    "taut": 20,
+    "min_size": 10.0
+  }
+}
+JSON
+
+# Run the Python analysis for this JID/calculator combination
+python run_chipsff.py --input_file input_${jid}_${calculator}.json
+
+EOT
+}
+
+# Function to submit a job for mlearn analysis
+submit_mlearn_job() {
+  local element=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=1-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=mlearn_${element}_${calculator}
+#SBATCH --output=logs/mlearn_${element}_${calculator}_%j.out
+#SBATCH --error=logs/mlearn_${element}_${calculator}_%j.err
+
+# Generate input JSON file for this mlearn element/calculator combination
+cat > input_mlearn_${element}_${calculator}.json <<JSON
+{
+  "mlearn_elements": ["$element"],
+  "calculator_type": "$calculator",
+  "properties_to_calculate": ["compare_mlearn_forces"]
+}
+JSON
+
+# Run the Python analysis for this mlearn element/calculator combination
+python run_chipsff.py --input_file input_mlearn_${element}_${calculator}.json
+
+EOT
+}
+
+# Loop through each JID and calculator combination for bulk calculations
+for jid in "${jid_list[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_bulk_job "$jid" "$calculator"
+  done
+done
+
+# Loop through each mlearn element and calculator combination for mlearn analysis
+for element in "${mlearn_elements[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_mlearn_job "$element" "$calculator"
+  done
+done
+
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/run_chipsff.py
new file mode 100644
index 000000000..79b65eb07
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_12_2_24/run_chipsff.py
@@ -0,0 +1,2286 @@
+#!/usr/bin/env python
+import os
+import subprocess
+import sys
+import time
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from ase.eos import EquationOfState
+from ase import Atoms as AseAtoms
+from ase.units import kJ
+from ase.constraints import ExpCellFilter
+from ase.optimize.fire import FIRE
+from ase.stress import voigt_6_to_full_3x3_stress
+import ase.units
+from jarvis.db.figshare import get_jid_data, data, get_request_data
+from jarvis.core.atoms import Atoms, ase_to_atoms
+from jarvis.io.vasp.inputs import Poscar
+from jarvis.core.kpoints import Kpoints3D as Kpoints
+from jarvis.analysis.defects.vacancy import Vacancy
+from jarvis.analysis.defects.surface import Surface
+import pandas as pd
+import h5py
+import shutil
+import glob
+import io
+import contextlib
+import requests
+import zipfile
+import re
+import plotly.express as px
+from sklearn.metrics import mean_absolute_error, r2_score
+from matplotlib.gridspec import GridSpec
+import argparse
+from jarvis.db.jsonutils import loadjson
+from chipsff.config import CHIPSFFConfig
+
+dft_3d = data("dft_3d")
+vacancydb = data("vacancydb")
+surf_url = "https://figshare.com/ndownloader/files/46355689"
+surface_data = get_request_data(js_tag="surface_db_dd.json", url=surf_url)
+
+def get_entry(jid):
+    for entry in dft_3d:
+        if entry["jid"] == jid:
+            return entry
+    raise ValueError(f"JID {jid} not found in the database")
+
+def collect_data(dft_3d, vacancydb, surface_data):
+    defect_ids = list(set([entry["jid"] for entry in vacancydb]))
+    surf_ids = list(set([entry["name"].split("Surface-")[1].split("_miller_")[0] for entry in surface_data]))
+
+    aggregated_data = []
+    for entry in dft_3d:
+        tmp = entry
+        tmp["vacancy"] = {}
+        tmp["surface"] = {}
+
+        # Check if the entry is in the defect dataset
+        if entry["jid"] in defect_ids:
+            for vac_entry in vacancydb:
+                if entry["jid"] == vac_entry["jid"]:
+                    tmp["vacancy"].setdefault(
+                        vac_entry["id"].split("_")[0] + "_" + vac_entry["id"].split("_")[1],
+                        vac_entry["ef"]
+                    )
+
+        # Check if the entry is in the surface dataset
+        if entry["jid"] in surf_ids:
+            for surf_entry in surface_data:
+                jid = surf_entry["name"].split("Surface-")[1].split("_miller_")[0]
+                if entry["jid"] == jid:
+                    tmp["surface"].setdefault(
+                        "_".join(surf_entry["name"].split("_thickness")[0].split("_")[0:5]),
+                        surf_entry["surf_en"]
+                    )
+
+        aggregated_data.append(tmp)
+    
+    return aggregated_data
+
+def get_vacancy_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the vacancy formation energy entry (vac_en_entry) for a given jid.
+    
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+    
+    Returns:
+    dict: A dictionary containing the vacancy formation energy entry and corresponding symbol.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            vacancy_data = entry.get("vacancy", {})
+            if vacancy_data:
+                return [{"symbol": key, "vac_en_entry": value} for key, value in vacancy_data.items()]
+            else:
+                return f"No vacancy data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+    
+def get_surface_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the surface energy entry (surf_en_entry) for a given jid.
+
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+
+    Returns:
+    list: A list of dictionaries containing the surface energy entry and corresponding name.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            surface_data = entry.get("surface", {})
+            if surface_data:
+                # Prepend 'Surface-JVASP-<jid>_' to the key for correct matching
+                return [{"name": f"{key}", "surf_en_entry": value} for key, value in surface_data.items()]
+            else:
+                return f"No surface data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+
+
+def log_job_info(message, log_file):
+    """Log job information to a file and print it."""
+    with open(log_file, "a") as f:
+        f.write(message + "\n")
+    print(message)
+
+def save_dict_to_json(data_dict, filename):
+    with open(filename, "w") as f:
+        json.dump(data_dict, f, indent=4)
+
+def load_dict_from_json(filename):
+    with open(filename, 'r') as f:
+        return json.load(f)
+        
+def setup_calculator(calculator_type):
+    if calculator_type == "matgl":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "matgl-direct":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-DIRECT-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "alignn_ff":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+        model_path = default_path() #can be adjusted to other ALIGNN models
+        return AlignnAtomwiseCalculator(
+            path=model_path,
+            stress_wt=0.1,
+            force_mult_natoms=False,
+            force_multiplier=1,
+            modl_filename="best_model.pt",
+        )
+    elif calculator_type == "alignn_ff_12_2_24":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+        return AlignnAtomwiseCalculator()
+    elif calculator_type == "chgnet":
+        from chgnet.model.dynamics import CHGNetCalculator
+        return CHGNetCalculator()
+    elif calculator_type == "mace":
+        from mace.calculators import mace_mp
+        return mace_mp()
+    elif calculator_type == "mace-alexandria":
+        from mace.calculators.mace import MACECalculator
+        model_path="/users/dtw2/utils/models/alexandria_v2/mace/2D_universal_force_field_cpu.model" #adjust path to mace-alexandria
+        return MACECalculator(model_path,device="cpu") 
+    elif calculator_type == "sevennet":
+        from sevenn.sevennet_calculator import SevenNetCalculator
+        checkpoint_path = "/users/dtw2/SevenNet/pretrained_potentials/SevenNet_0__11July2024/checkpoint_sevennet_0.pth" #adjust path to sevennet
+        return SevenNetCalculator(checkpoint_path, device="cpu")
+    elif calculator_type == "orb-v2":
+        from orb_models.forcefield import pretrained
+        from orb_models.forcefield.calculator import ORBCalculator
+        orbff = pretrained.orb_v2()
+        return ORBCalculator(orbff, device="cpu")
+    elif calculator_type == "eqV2_31M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat.pt") #adjust path to OMat24   
+    elif calculator_type == "eqV2_153M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_153M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_31M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat_mp_salex.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat_mp_salex.pt") #adjust path to OMat24
+    else:
+        raise ValueError("Unsupported calculator type")
+
+class MaterialsAnalyzer:
+    def __init__(
+        self,
+        jid=None,
+        calculator_type=None,
+        chemical_potentials_file=None,
+        film_jid=None,
+        substrate_jid=None,
+        film_index=None,
+        substrate_index=None,
+        bulk_relaxation_settings=None,
+        phonon_settings=None,
+        properties_to_calculate=None,
+        use_conventional_cell=False,
+        surface_settings=None,
+        defect_settings=None,
+        phonon3_settings=None,
+        md_settings=None
+    ):
+        self.calculator_type = calculator_type
+        self.use_conventional_cell = use_conventional_cell
+        self.chemical_potentials_file = chemical_potentials_file
+        self.bulk_relaxation_settings = bulk_relaxation_settings or {}
+        self.phonon_settings = phonon_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.properties_to_calculate = properties_to_calculate or []
+        self.surface_settings = surface_settings or {}
+        self.defect_settings = defect_settings or {}
+        self.film_index = film_index or "1_1_0"
+        self.substrate_index = substrate_index or "1_1_0"
+        self.phonon3_settings = phonon3_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.md_settings = md_settings or {'dt': 1, 'temp0': 3500, 'nsteps0': 1000, 'temp1': 300, 'nsteps1': 2000, 'taut': 20, 'min_size': 10.0}
+        if jid:
+            self.jid = jid
+            # Load atoms for the given JID
+            self.atoms = self.get_atoms(jid)
+            # Get reference data for the material
+            self.reference_data = get_entry(jid)
+            # Set up the output directory and log file
+            self.output_dir = f"{jid}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(self.output_dir, f"{jid}_job_log.txt")
+            # Initialize job_info dictionary
+            self.job_info = {
+                "jid": jid,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        elif film_jid and substrate_jid:
+            # Ensure film_jid and substrate_jid are strings, not lists
+            if isinstance(film_jid, list):
+                film_jid = film_jid[0]
+            if isinstance(substrate_jid, list):
+                substrate_jid = substrate_jid[0]
+
+            self.film_jid = film_jid
+            self.substrate_jid = substrate_jid
+
+            # Include Miller indices in directory and file names
+            self.output_dir = f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(
+                self.output_dir,
+                f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_job_log.txt",
+            )
+            self.job_info = {
+                "film_jid": film_jid,
+                "substrate_jid": substrate_jid,
+                "film_index": self.film_index,
+                "substrate_index": self.substrate_index,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        else:
+            raise ValueError("Either 'jid' or both 'film_jid' and 'substrate_jid' must be provided.")
+
+        # Set up the logger
+        self.setup_logger()
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger(self.jid or f"{self.film_jid}_{self.substrate_jid}")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        """Log information to the job log file."""
+        log_job_info(message, self.log_file)
+
+    def get_atoms(self, jid):
+        dat = get_jid_data(jid=jid, dataset="dft_3d")
+        return Atoms.from_dict(dat["atoms"])
+
+    def setup_calculator(self):
+        calc = setup_calculator(self.calculator_type)
+        self.log(f"Using calculator: {self.calculator_type}")
+        return calc
+
+    def load_chemical_potentials(self):
+        if os.path.exists(self.chemical_potentials_file):
+            with open(self.chemical_potentials_file, "r") as f:
+                return json.load(f)
+        else:
+            return {}
+
+    def save_chemical_potentials(self):
+        with open(self.chemical_potentials_file, "w") as f:
+            json.dump(self.chemical_potentials, f, indent=4)
+
+    def capture_fire_output(self, ase_atoms, fmax, steps):
+        """Capture the output of the FIRE optimizer."""
+        log_stream = io.StringIO()
+        with contextlib.redirect_stdout(log_stream):
+            dyn = FIRE(ase_atoms)
+            dyn.run(fmax=fmax, steps=steps)
+        output = log_stream.getvalue().strip()
+
+        final_energy = None
+        if output:
+            last_line = output.split("\n")[-1]
+            match = re.search(r"FIRE:\s+\d+\s+\d+:\d+:\d+\s+(-?\d+\.\d+)", last_line)
+            if match:
+                final_energy = float(match.group(1))
+
+        return final_energy, dyn.nsteps
+
+    def relax_structure(self):
+        """Perform structure relaxation and log the process."""
+        self.log(f"Starting relaxation for {self.jid}")
+
+        # Use conventional cell if specified
+
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for relaxation.")
+            self.atoms = self.atoms.get_conventional_atoms  # or appropriate method
+
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.bulk_relaxation_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.bulk_relaxation_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', False)
+        ase_atoms = self.atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+
+        # Run FIRE optimizer and capture the output using relaxation settings
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < steps
+
+        # Log the final energy and relaxation status
+        self.log(f"Final energy of FIRE optimization for structure: {final_energy}")
+        self.log(
+            f"Relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        # Update job info and save the relaxed structure
+        self.job_info["relaxed_atoms"] = relaxed_atoms.to_dict()
+        self.job_info["final_energy_structure"] = final_energy
+        self.job_info["converged"] = converged
+        self.log(f"Relaxed structure: {relaxed_atoms.to_dict()}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return relaxed_atoms if converged else None
+
+
+
+    def calculate_formation_energy(self, relaxed_atoms):
+        """
+        Calculate the formation energy per atom using the equilibrium energy and chemical potentials.
+        """
+        e0 = self.job_info["equilibrium_energy"]
+        composition = relaxed_atoms.composition.to_dict()
+        total_energy = e0
+
+        for element, amount in composition.items():
+            chemical_potential = self.get_chemical_potential(element)
+            if chemical_potential is None:
+                self.log(f"Skipping formation energy calculation due to missing chemical potential for {element}.")
+                continue  # Or handle this appropriately
+            total_energy -= chemical_potential * amount
+
+        formation_energy_per_atom = total_energy / relaxed_atoms.num_atoms
+
+    # Log and save the formation energy
+        self.job_info["formation_energy_per_atom"] = formation_energy_per_atom
+        self.log(f"Formation energy per atom: {formation_energy_per_atom}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return formation_energy_per_atom
+
+    def calculate_element_chemical_potential(self, element, element_jid):
+        """
+        Calculate the chemical potential of a pure element using its standard structure.
+        """
+        self.log(f"Calculating chemical potential for element: {element} using JID: {element_jid}")
+        try:
+            # Get standard structure for the element using the provided JID
+            element_atoms = self.get_atoms(element_jid)
+            ase_atoms = element_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+
+            # Perform energy calculation
+            energy = ase_atoms.get_potential_energy() / len(ase_atoms)
+            self.log(f"Calculated chemical potential for {element}: {energy} eV/atom")
+            return energy
+        except Exception as e:
+            self.log(f"Error calculating chemical potential for {element}: {e}")
+            return None
+
+    def get_chemical_potential(self, element):
+        """Fetch chemical potential from JSON based on the element and calculator."""
+        element_data = self.chemical_potentials.get(element, {})
+        chemical_potential = element_data.get(f"energy_{self.calculator_type}")
+
+        if chemical_potential is None:
+            self.log(
+                f"No chemical potential found for {element} with calculator {self.calculator_type}, calculating it now..."
+            )
+            # Get standard JID for the element from chemical_potentials.json
+            element_jid = element_data.get("jid")
+            if element_jid is None:
+                self.log(f"No standard JID found for element {element} in chemical_potentials.json")
+                return None  # Skip this element
+
+            # Calculate chemical potential
+            chemical_potential = self.calculate_element_chemical_potential(element, element_jid)
+            if chemical_potential is None:
+                self.log(f"Failed to calculate chemical potential for {element}")
+                return None
+            # Add it to the chemical potentials dictionary
+            if element not in self.chemical_potentials:
+                self.chemical_potentials[element] = {}
+            self.chemical_potentials[element][f"energy_{self.calculator_type}"] = chemical_potential
+            # Save the updated chemical potentials to file
+            self.save_chemical_potentials()
+
+        return chemical_potential
+    def calculate_forces(self, atoms):
+        """
+        Calculate the forces on the given atoms without performing relaxation.
+        """
+        self.log(f"Calculating forces for {self.jid}")
+
+    # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+    # Calculate forces
+        forces = ase_atoms.get_forces()  # This returns an array of forces
+
+    # Log and save the forces
+        self.job_info['forces'] = forces.tolist()  # Convert to list for JSON serialization
+        self.log(f"Forces calculated: {forces}")
+
+    # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return forces
+
+    def calculate_ev_curve(self, relaxed_atoms):
+        """Calculate the energy-volume (E-V) curve and log results."""
+        self.log(f"Calculating EV curve for {self.jid}")
+
+        dx = np.arange(-0.06, 0.06, 0.01)  # Strain values
+        y = []  # Energies
+        vol = []  # Volumes
+        strained_structures = []  # To store strained structures
+
+        for i in dx:
+            # Apply strain and calculate energy at each strain value
+            strained_atoms = relaxed_atoms.strain_atoms(i)
+            strained_structures.append(strained_atoms)
+            ase_atoms = strained_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+            energy = ase_atoms.get_potential_energy()
+
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+        # Convert data to numpy arrays for processing
+        y = np.array(y)
+        vol = np.array(vol)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        try:
+            eos = EquationOfState(vol, y, eos="murnaghan")
+            v0, e0, B = eos.fit()
+
+            # Bulk modulus in GPa (conversion factor from ASE units)
+            kv = B / kJ * 1.0e24  # Convert to GPa
+
+            # Log important results
+            self.log(f"Bulk modulus: {kv} GPa")
+            self.log(f"Equilibrium energy: {e0} eV")
+            self.log(f"Equilibrium volume: {v0} ų")
+
+            # Save E-V curve plot
+            fig = plt.figure()
+            eos.plot()
+            ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+            fig.savefig(ev_plot_filename)
+            plt.close(fig)
+            self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+            # Save E-V curve data to a text file
+            ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+            with open(ev_data_filename, "w") as f:
+                f.write("Volume (ų)\tEnergy (eV)\n")
+                for v, e in zip(vol, y):
+                    f.write(f"{v}\t{e}\n")
+            self.log(f"E-V curve data saved to {ev_data_filename}")
+
+            # Update job info with the results
+            self.job_info["bulk_modulus"] = kv
+            self.job_info["equilibrium_energy"] = e0
+            self.job_info["equilibrium_volume"] = v0
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        except RuntimeError as e:
+            self.log(f"Error fitting EOS for {self.jid}: {e}")
+            self.log("Skipping bulk modulus calculation due to fitting error.")
+            kv = None  # Set bulk modulus to None or handle this as you wish
+            e0, v0 = None, None  # Set equilibrium energy and volume to None
+
+        # Return additional values for thermal expansion analysis
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+
+    def calculate_elastic_tensor(self, relaxed_atoms):
+        import elastic
+        from elastic import get_elementary_deformations, get_elastic_tensor
+        """
+        Calculate the elastic tensor for the relaxed structure using the provided calculator.
+        """
+        self.log(f"Starting elastic tensor calculation for {self.jid}")
+        start_time = time.time()
+
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Get elementary deformations for elastic tensor calculation
+        systems = elastic.get_elementary_deformations(ase_atoms)
+
+        # Calculate the elastic tensor and convert to GPa
+        cij_order = elastic.elastic.get_cij_order(ase_atoms)
+        Cij, Bij = elastic.get_elastic_tensor(ase_atoms, systems)
+        elastic_tensor = {
+            i: j / ase.units.GPa for i, j in zip(cij_order, Cij)
+        }  # Convert to GPa
+
+        # Save and log the results
+        self.job_info["elastic_tensor"] = elastic_tensor
+        self.log(
+            f"Elastic tensor for {self.jid} with {self.calculator_type}: {elastic_tensor}"
+        )
+
+        # Timing the calculation
+        end_time = time.time()
+        self.log(f"Elastic Calculation time: {end_time - start_time} seconds")
+
+        # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return elastic_tensor
+
+    def run_phonon_analysis(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Perform Phonon calculation, generate force constants, and plot band structure & DOS."""
+        self.log(f"Starting phonon analysis for {self.jid}")
+        phonopy_bands_figname = f"ph_{self.jid}_{self.calculator_type}.png"
+
+        # Phonon generation parameters
+        dim = self.phonon_settings.get('dim', [2, 2, 2])
+        # Define the conversion factor from THz to cm^-1
+        THz_to_cm = 33.35641  # 1 THz = 33.35641 cm^-1
+
+        force_constants_filename = "FORCE_CONSTANTS"
+        eigenvalues_filename = "phonon_eigenvalues.txt"
+        thermal_props_filename = "thermal_properties.txt"
+        write_fc = True
+        min_freq_tol_cm = -5.0  # in cm^-1
+        distance = self.phonon_settings.get('distance', 0.2)
+
+        # Generate k-point path
+        kpoints = Kpoints().kpath(relaxed_atoms, line_density=5)
+
+        # Convert atoms to Phonopy-compatible object
+        self.log("Converting atoms to Phonopy-compatible format...")
+        bulk = relaxed_atoms.phonopy_converter()
+        phonon = Phonopy(
+            bulk,
+            [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]],
+            # Do not set factor here to keep frequencies in THz during calculations
+        )
+
+        # Generate displacements
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+        self.log(f"Generated {len(supercells)} supercells for displacements.")
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for idx, scell in enumerate(supercells):
+            self.log(f"Calculating forces for supercell {idx+1}...")
+            ase_atoms = AseAtoms(
+                symbols=scell.symbols,
+                positions=scell.positions,
+                cell=scell.cell,
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+
+            # Correct for drift force
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+
+            set_of_forces.append(forces)
+
+        # Generate force constants
+        self.log("Producing force constants...")
+        phonon.produce_force_constants(forces=set_of_forces)
+
+        # Write force constants to file if required
+        if write_fc:
+            force_constants_filepath = os.path.join(
+                self.output_dir, force_constants_filename
+            )
+            self.log(f"Writing force constants to {force_constants_filepath}...")
+            write_FORCE_CONSTANTS(
+                phonon.force_constants, filename=force_constants_filepath
+            )
+            self.log(f"Force constants saved to {force_constants_filepath}")
+
+        # Prepare bands for band structure calculation
+        bands = [kpoints.kpts]  # Assuming kpoints.kpts is a list of q-points
+
+        # Prepare labels and path_connections
+        labels = []
+        path_connections = []
+        for i, label in enumerate(kpoints.labels):
+            if label:
+                labels.append(label)
+            else:
+                labels.append("")  # Empty string for points without labels
+
+        # Since we have a single path, set path_connections accordingly
+        path_connections = [True] * (len(bands) - 1)
+        path_connections.append(False)
+
+        # Run band structure calculation with labels
+        self.log("Running band structure calculation...")
+        phonon.run_band_structure(
+            bands,
+            with_eigenvectors=False,
+            labels=labels,
+            path_connections=path_connections,
+        )
+
+        # Write band.yaml file (frequencies will be in THz)
+        band_yaml_filepath = os.path.join(self.output_dir, "band.yaml")
+        self.log(f"Writing band structure data to {band_yaml_filepath}...")
+        phonon.band_structure.write_yaml(filename=band_yaml_filepath)
+        self.log(f"band.yaml saved to {band_yaml_filepath}")
+
+        # --- Begin post-processing to convert frequencies to cm^-1 while preserving formatting ---
+        from ruamel.yaml import YAML
+
+        self.log(f"Converting frequencies in {band_yaml_filepath} to cm^-1 while preserving formatting...")
+        yaml = YAML()
+        yaml.preserve_quotes = True
+
+        with open(band_yaml_filepath, 'r') as f:
+            band_data = yaml.load(f)
+
+        for phonon_point in band_data['phonon']:
+            for band in phonon_point['band']:
+                freq = band['frequency']
+                if freq is not None:
+                    band['frequency'] = freq * THz_to_cm
+
+        with open(band_yaml_filepath, 'w') as f:
+            yaml.dump(band_data, f)
+
+        self.log(f"Frequencies in {band_yaml_filepath} converted to cm^-1 with formatting preserved")
+        # --- End post-processing ---
+
+        # Phonon band structure and eigenvalues
+        lbls = kpoints.labels
+        lbls_ticks = []
+        freqs = []
+        lbls_x = []
+        count = 0
+        eigenvalues = []
+
+        for ii, k in enumerate(kpoints.kpts):
+            k_str = ",".join(map(str, k))
+            if ii == 0 or k_str != ",".join(map(str, kpoints.kpts[ii - 1])):
+                freqs_at_k = phonon.get_frequencies(k)  # Frequencies in THz
+                freqs_at_k_cm = freqs_at_k * THz_to_cm  # Convert to cm^-1
+                freqs.append(freqs_at_k_cm)
+                eigenvalues.append((k, freqs_at_k_cm))  # Store frequencies in cm^-1
+                lbl = "$" + str(lbls[ii]) + "$" if lbls[ii] else ""
+                if lbl:
+                    lbls_ticks.append(lbl)
+                    lbls_x.append(count)
+                count += 1
+
+        # Write eigenvalues to file with frequencies in cm^-1
+        eigenvalues_filepath = os.path.join(self.output_dir, eigenvalues_filename)
+        self.log(f"Writing phonon eigenvalues to {eigenvalues_filepath}...")
+        with open(eigenvalues_filepath, "w") as eig_file:
+            eig_file.write("k-points\tFrequencies (cm^-1)\n")
+            for k, freqs_at_k_cm in eigenvalues:
+                k_str = ",".join(map(str, k))
+                freqs_str = "\t".join(map(str, freqs_at_k_cm))
+                eig_file.write(f"{k_str}\t{freqs_str}\n")
+        self.log(f"Phonon eigenvalues saved to {eigenvalues_filepath}")
+
+        # Convert frequencies to numpy array in cm^-1
+        freqs = np.array(freqs)
+
+        # Plot phonon band structure and DOS
+        the_grid = plt.GridSpec(1, 2, width_ratios=[3, 1], wspace=0.0)
+        plt.rcParams.update({"font.size": 18})
+        plt.figure(figsize=(10, 5))
+
+        # Plot phonon bands
+        plt.subplot(the_grid[0])
+        for i in range(freqs.shape[1]):
+            plt.plot(freqs[:, i], lw=2, c="b")
+        for i in lbls_x:
+            plt.axvline(x=i, c="black")
+        plt.xticks(lbls_x, lbls_ticks)
+        plt.ylabel("Frequency (cm$^{-1}$)")
+        plt.xlim([0, max(lbls_x)])
+
+        # Run mesh and DOS calculations
+        phonon.run_mesh([40, 40, 40], is_gamma_center=True, is_mesh_symmetry=False)
+        phonon.run_total_dos()
+        tdos = phonon.total_dos
+        freqs_dos = np.array(tdos.frequency_points) * THz_to_cm  # Convert to cm^-1
+        dos_values = tdos.dos
+        min_freq = min_freq_tol_cm  # in cm^-1
+        max_freq = max(freqs_dos)
+
+        plt.ylim([min_freq, max_freq])
+
+        # Plot DOS
+        plt.subplot(the_grid[1])
+        plt.fill_between(
+            dos_values, freqs_dos, color=(0.2, 0.4, 0.6, 0.6), edgecolor="k", lw=1, y2=0
+        )
+        plt.xlabel("DOS")
+        plt.yticks([])
+        plt.xticks([])
+        plt.ylim([min_freq, max_freq])
+        plt.xlim([0, max(dos_values)])
+
+        # Save the plot
+        os.makedirs(self.output_dir, exist_ok=True)
+        plot_filepath = os.path.join(self.output_dir, phonopy_bands_figname)
+        plt.tight_layout()
+        plt.savefig(plot_filepath)
+        self.log(
+            f"Phonon band structure and DOS combined plot saved to {plot_filepath}"
+        )
+        plt.close()
+
+        self.log("Calculating thermal properties...")
+        phonon.run_mesh(mesh=[20, 20, 20])
+        phonon.run_thermal_properties(t_step=10, t_max=1000, t_min=0)
+        tprop_dict = phonon.get_thermal_properties_dict()
+
+        # Plot thermal properties
+        plt.figure()
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["free_energy"],
+            label="Free energy (kJ/mol)",
+            color="red",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["entropy"],
+            label="Entropy (J/K*mol)",
+            color="blue",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["heat_capacity"],
+            label="Heat capacity (J/K*mol)",
+            color="green",
+        )
+        plt.legend()
+        plt.xlabel("Temperature (K)")
+        plt.ylabel("Thermal Properties")
+        plt.title("Thermal Properties")
+
+        thermal_props_plot_filepath = os.path.join(
+            self.output_dir, f"Thermal_Properties_{self.jid}.png"
+        )
+        plt.savefig(thermal_props_plot_filepath)
+        self.log(f"Thermal properties plot saved to {thermal_props_plot_filepath}")
+        plt.close()
+
+        # Save thermal properties to file
+        thermal_props_filepath = os.path.join(self.output_dir, thermal_props_filename)
+        with open(thermal_props_filepath, "w") as f:
+            f.write(
+                "Temperature (K)\tFree Energy (kJ/mol)\tEntropy (J/K*mol)\tHeat Capacity (J/K*mol)\n"
+            )
+            for i in range(len(tprop_dict["temperatures"])):
+                f.write(
+                    f"{tprop_dict['temperatures'][i]}\t{tprop_dict['free_energy'][i]}\t"
+                    f"{tprop_dict['entropy'][i]}\t{tprop_dict['heat_capacity'][i]}\n"
+                )
+        self.log(f"Thermal properties written to {thermal_props_filepath}")
+
+        # Calculate zero-point energy (ZPE)
+        zpe = tprop_dict["free_energy"][0] * 0.0103643  # Converting from kJ/mol to eV
+        self.log(f"Zero-point energy: {zpe} eV")
+
+        # Save to job info
+        self.job_info["phonopy_bands"] = phonopy_bands_figname
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return phonon, zpe
+
+    def analyze_defects(self):
+        """Analyze defects by generating, relaxing, and calculating vacancy formation energy."""
+        self.log("Starting defect analysis...")
+        generate_settings = self.defect_settings.get('generate_settings', {})
+        on_conventional_cell = generate_settings.get('on_conventional_cell', True)
+        enforce_c_size = generate_settings.get('enforce_c_size', 8)
+        extend = generate_settings.get('extend', 1)
+    # Generate defect structures from the original atoms
+        defect_structures = Vacancy(self.atoms).generate_defects(on_conventional_cell=on_conventional_cell, enforce_c_size=enforce_c_size, extend=extend)
+
+        for defect in defect_structures:
+        # Extract the defect structure and related metadata
+            defect_structure = Atoms.from_dict(defect.to_dict()["defect_structure"])
+        
+        # Construct a consistent defect name without Wyckoff notation
+            element = defect.to_dict()['symbol']
+            defect_name = f"{self.jid}_{element}"  # Consistent format
+            self.log(f"Analyzing defect: {defect_name}")
+
+        # Relax the defect structure
+            relaxed_defect_atoms = self.relax_defect_structure(defect_structure, name=defect_name)
+
+            if relaxed_defect_atoms is None:
+                self.log(f"Skipping {defect_name} due to failed relaxation.")
+                continue
+
+        # Retrieve energies for calculating the vacancy formation energy
+            vacancy_energy = self.job_info.get(f"final_energy_defect for {defect_name}")
+            bulk_energy = (
+                self.job_info.get("equilibrium_energy")
+                / self.atoms.num_atoms
+                * (defect_structure.num_atoms + 1)
+            )
+
+            if vacancy_energy is None or bulk_energy is None:
+                self.log(f"Skipping {defect_name} due to missing energy values.")
+                continue
+
+        # Get chemical potential and calculate vacancy formation energy
+            chemical_potential = self.get_chemical_potential(element)
+
+            if chemical_potential is None:
+                self.log(f"Skipping {defect_name} due to missing chemical potential for {element}.")
+                continue
+
+            vacancy_formation_energy = vacancy_energy - bulk_energy + chemical_potential
+
+        # Log and store the vacancy formation energy consistently
+            self.job_info[f"vacancy_formation_energy for {defect_name}"] = vacancy_formation_energy
+            self.log(f"Vacancy formation energy for {defect_name}: {vacancy_formation_energy} eV")
+
+    # Save the job info to a JSON file
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+        self.log("Defect analysis completed.")
+
+    def relax_defect_structure(self, atoms, name):
+        """Relax the defect structure and log the process."""
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.defect_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.defect_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log the final energy and relaxation status
+        self.log(
+            f"Final energy of FIRE optimization for defect structure: {final_energy}"
+        )
+        self.log(
+            f"Defect relaxation {'converged' if converged else 'did not converge'} within 200 steps."
+        )
+
+        # Update job info with the final energy and convergence status
+        self.job_info[f"final_energy_defect for {name}"] = final_energy
+        self.job_info[f"converged for {name}"] = converged
+
+        if converged:
+            poscar_filename = os.path.join(self.output_dir, f"POSCAR_{name}_relaxed.vasp")
+            poscar_defect = Poscar(relaxed_atoms)
+            poscar_defect.write_file(poscar_filename)
+            self.log(f"Relaxed defect structure saved to {poscar_filename}")
+
+        return relaxed_atoms if converged else None
+
+    def analyze_surfaces(self):
+        """
+        Perform surface analysis by generating surface structures, relaxing them, and calculating surface energies.
+        """
+        self.log(f"Analyzing surfaces for {self.jid}")
+
+        indices_list = self.surface_settings.get('indices_list', [
+            [1, 0, 0],
+            [1, 1, 1],
+            [1, 1, 0],
+            [0, 1, 1],
+            [0, 0, 1],
+            [0, 1, 0],
+        ])
+        layers = self.surface_settings.get('layers', 4)
+        vacuum = self.surface_settings.get('vacuum', 18)
+
+        for indices in indices_list:
+            # Generate surface and check for polarity
+            surface = (
+                Surface(atoms=self.atoms, indices=indices, layers=layers, vacuum=vacuum)
+                .make_surface()
+                .center_around_origin()
+            )
+            if surface.check_polar:
+                self.log(
+                    f"Skipping polar surface for {self.jid} with indices {indices}"
+                )
+                continue
+
+            # Write initial POSCAR for surface
+            poscar_surface = Poscar(atoms=surface)
+            poscar_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}.vasp",
+                )
+            )
+
+            # Relax the surface structure
+            relaxed_surface_atoms, final_energy = self.relax_surface_structure(
+                surface, indices
+            )
+
+            # If relaxation failed, skip further calculations
+            if relaxed_surface_atoms is None:
+                self.log(f"Skipping surface {indices} due to failed relaxation.")
+                continue
+
+            # Write relaxed POSCAR for surface
+            pos_relaxed_surface = Poscar(relaxed_surface_atoms)
+            pos_relaxed_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}_relaxed.vasp",
+                )
+            )
+
+            # Calculate and log surface energy
+            bulk_energy = self.job_info.get("equilibrium_energy")
+            if final_energy is None or bulk_energy is None:
+                self.log(
+                    f"Skipping surface energy calculation for {self.jid} with indices {indices} due to missing energy values."
+                )
+                continue
+
+            surface_energy = self.calculate_surface_energy(
+                final_energy, bulk_energy, relaxed_surface_atoms, surface
+            )
+
+            # Store the surface energy with the new naming convention
+            surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+            self.job_info[surface_name] = surface_energy
+            self.log(
+                f"Surface energy for {self.jid} with indices {indices}: {surface_energy} J/m^2"
+            )
+
+        # Save updated job info
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log("Surface analysis completed.")
+
+    def relax_surface_structure(self, atoms, indices):
+        """
+        Relax the surface structure and log the process.
+        """
+        filter_type = self.surface_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.surface_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        self.log(f"Starting surface relaxation for {self.jid} with indices {indices}")
+        start_time = time.time()
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+        # Implement other filters if needed
+            pass
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log relaxation results
+        self.log(
+            f"Final energy of FIRE optimization for surface structure: {final_energy}"
+        )
+        self.log(
+            f"Surface relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        end_time = time.time()
+        self.log(
+            f"Surface Relaxation Calculation time: {end_time - start_time} seconds"
+        )
+
+        # Update job info and return relaxed atoms if converged, otherwise return None
+        self.job_info[f"final_energy_surface_{indices}"] = final_energy
+        self.job_info[f"converged_surface_{indices}"] = converged
+
+        # Return both relaxed atoms and the final energy as a tuple
+        return (relaxed_atoms if converged else None), final_energy
+
+    def calculate_surface_energy(
+        self, final_energy, bulk_energy, relaxed_atoms, surface
+    ):
+        """
+        Calculate the surface energy based on the final energy of the relaxed surface and bulk energy.
+        """
+        # Calculate the number of bulk units in the surface supercell
+        num_units = surface.num_atoms / self.atoms.num_atoms
+
+        # Calculate the surface area using the lattice vectors
+        m = relaxed_atoms.lattice.matrix
+        surface_area = np.linalg.norm(np.cross(m[0], m[1]))
+
+        # Calculate surface energy in J/m^2
+        surface_energy = (
+            (final_energy - bulk_energy * num_units) * 16.02176565 / (2 * surface_area)
+        )
+
+        return surface_energy
+
+    def run_phonon3_analysis(self, relaxed_atoms):
+        from phono3py import Phono3py
+        """Run Phono3py analysis, process results, and generate thermal conductivity data."""
+        self.log(f"Starting Phono3py analysis for {self.jid}")
+
+        # Set parameters for the Phono3py calculation
+        dim = self.phonon3_settings.get('dim', [2, 2, 2])
+        distance = self.phonon3_settings.get('distance', 0.2)
+
+        #force_multiplier = 16
+
+        # Convert atoms to Phonopy-compatible object and set up Phono3py
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        bulk = relaxed_atoms.phonopy_converter()
+
+        phonon = Phono3py(bulk, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]])
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for scell in supercells:
+            ase_atoms = AseAtoms(
+                symbols=scell.get_chemical_symbols(),
+                scaled_positions=scell.get_scaled_positions(),
+                cell=scell.get_cell(),
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+            set_of_forces.append(forces)
+
+        # Set the forces and produce third-order force constants
+        forces = np.array(set_of_forces).reshape(-1, len(phonon.supercell), 3)
+        phonon.forces = forces
+        phonon.produce_fc3()
+
+        # Run thermal conductivity calculation
+        phonon.mesh_numbers = 30
+        phonon.init_phph_interaction()
+        phonon.run_thermal_conductivity(
+            temperatures=range(0, 1001, 10), write_kappa=True
+        )
+
+        # Collect thermal conductivity data
+        kappa = phonon.thermal_conductivity.kappa
+        self.log(f"Thermal conductivity: {kappa}")
+
+        # Move generated HDF5 files to the output directory
+        hdf5_file_pattern = "kappa-*.hdf5"
+        for hdf5_file in glob.glob(hdf5_file_pattern):
+            shutil.move(hdf5_file, os.path.join(self.output_dir, hdf5_file))
+
+        # Process Phono3py results and save plots
+        self.process_phonon3_results()
+
+        # Save updated job info to JSON
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(f"Phono3py analysis completed for {self.jid}")
+
+    def process_phonon3_results(self):
+        """Process Phono3py results and generate plots of thermal conductivity."""
+        file_pattern = os.path.join(self.output_dir, "kappa-*.hdf5")
+        file_list = glob.glob(file_pattern)
+
+        temperatures = np.arange(10, 101, 10)
+        kappa_xx_values = []
+
+        if file_list:
+            hdf5_filename = file_list[0]
+            self.log(f"Processing file: {hdf5_filename}")
+
+            for temperature_index in temperatures:
+                converted_kappa = self.convert_kappa_units(
+                    hdf5_filename, temperature_index
+                )
+                kappa_xx = converted_kappa[0]
+                kappa_xx_values.append(kappa_xx)
+                self.log(
+                    f"Temperature index {temperature_index}, converted kappa: {kappa_xx}"
+                )
+
+            # Save results to job_info
+            self.job_info["temperatures"] = temperatures.tolist()
+            self.job_info["kappa_xx_values"] = kappa_xx_values
+
+            # Plot temperature vs. converted kappa (xx element)
+            plt.figure(figsize=(8, 6))
+            plt.plot(
+                temperatures * 10, kappa_xx_values, marker="o", linestyle="-", color="b"
+            )
+            plt.xlabel("Temperature (K)")
+            plt.ylabel("Converted Kappa (xx element)")
+            plt.title("Temperature vs. Converted Kappa (xx element)")
+            plt.grid(True)
+            plt.savefig(
+                os.path.join(self.output_dir, "Temperature_vs_Converted_Kappa.png")
+            )
+            plt.close()
+        else:
+            self.log("No files matching the pattern were found.")
+
+    def convert_kappa_units(self, hdf5_filename, temperature_index):
+        """Convert thermal conductivity kappa from HDF5 file units."""
+        with h5py.File(hdf5_filename, "r") as f:
+            kappa_unit_conversion = f["kappa_unit_conversion"][()]
+            heat_capacity = f["heat_capacity"][:]
+            gv_by_gv = f["gv_by_gv"][:]
+            gamma = f["gamma"][:]
+
+            converted_kappa = (
+                kappa_unit_conversion
+                * heat_capacity[temperature_index, 2, 0]
+                * gv_by_gv[2, 0]
+                / (2 * gamma[temperature_index, 2, 0])
+            )
+
+            return converted_kappa
+
+    def calculate_thermal_expansion(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Calculate the thermal expansion coefficient using QHA."""
+
+        def log(message):
+            with open(self.log_file, "a") as f:
+                f.write(message + "\n")
+            print(message)
+
+        log("Starting thermal expansion analysis...")
+
+        # Step 1: Calculate finer E-V curve
+        vol, y, strained_structures, eos, kv, e0, v0 = self.fine_ev_curve(
+            atoms=relaxed_atoms, dx=np.linspace(-0.05, 0.05, 50)  # Denser grid
+        )
+
+        # Log Bulk modulus, equilibrium energy, and volume
+        log(
+            f"Bulk modulus: {kv} GPa, Equilibrium energy: {y[0]} eV, Volume: {vol[0]} ų"
+        )
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = y[0]
+        self.job_info["equilibrium_volume"] = vol[0]
+
+        # Step 2: Generate phonons for strained structures
+        free_energies, heat_capacities, entropies, temperatures = (
+            self.generate_phonons_for_volumes(
+                strained_structures,
+                calculator=self.calculator,
+                dim=[2, 2, 2],
+                distance=0.2,
+                mesh=[20, 20, 20],
+            )
+        )
+
+        # Step 3: Perform QHA-based thermal expansion analysis
+        alpha = self.perform_qha(
+            volumes=vol,
+            energies=y,
+            free_energies=free_energies,
+            heat_capacities=heat_capacities,
+            entropies=entropies,
+            temperatures=temperatures,
+            output_dir=self.output_dir,
+        )
+
+        self.log(f"Thermal expansion coefficient calculated: {alpha}")
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(
+            f"Thermal expansion analysis information saved to file: {self.jid}_{self.calculator_type}_job_info.json"
+        )
+
+    # Helper Functions Inside the Class
+    def fine_ev_curve(self, atoms, dx=np.linspace(-0.05, 0.05, 50)):
+        """
+        Generate a finer energy-volume curve for strained structures.
+        """
+        y = []
+        vol = []
+        strained_structures = []
+
+        for i in dx:
+            # Apply strain and get strained atoms
+            strained_atoms = atoms.strain_atoms(i)
+            ase_atoms = strained_atoms.ase_converter()  # Convert to ASE Atoms
+            ase_atoms.calc = self.calculator  # Assign the calculator
+
+            # Get potential energy and volume
+            energy = ase_atoms.get_potential_energy()
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+            strained_structures.append(strained_atoms)  # Save the strained structure
+
+        vol = np.array(vol)
+        y = np.array(y)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        eos = EquationOfState(vol, y, eos="murnaghan")
+        v0, e0, B = eos.fit()
+        kv = B / kJ * 1.0e24  # Convert to GPa
+
+        # Log important results
+        self.log(f"Bulk modulus: {kv} GPa")
+        self.log(f"Equilibrium energy: {e0} eV")
+        self.log(f"Equilibrium volume: {v0} ų")
+
+        # Save E-V curve plot
+        fig = plt.figure()
+        eos.plot()
+        ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+        fig.savefig(ev_plot_filename)
+        plt.close(fig)
+        self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+        # Save E-V curve data to a text file
+        ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+        with open(ev_data_filename, "w") as f:
+            f.write("Volume (ų)\tEnergy (eV)\n")
+            for v, e in zip(vol, y):
+                f.write(f"{v}\t{e}\n")
+        self.log(f"E-V curve data saved to {ev_data_filename}")
+
+        # Update job info with the results
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = e0
+        self.job_info["equilibrium_volume"] = v0
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+    def generate_phonons_for_volumes(
+        self, structures, calculator, dim=[2, 2, 2], distance=0.2, mesh=[20, 20, 20]
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        all_free_energies = []
+        all_heat_capacities = []
+        all_entropies = []
+        temperatures = np.arange(0, 300, 6)  # Define temperature range
+
+        for structure in structures:
+            # Convert structure to PhonopyAtoms
+            phonopy_atoms = PhonopyAtoms(
+                symbols=[str(e) for e in structure.elements],
+                positions=structure.cart_coords,
+                cell=structure.lattice.matrix,
+            )
+
+            # Initialize Phonopy object
+            phonon = Phonopy(
+                phonopy_atoms, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]]
+            )
+            phonon.generate_displacements(distance=distance)
+
+            # Calculate forces for displaced structures
+            supercells = phonon.get_supercells_with_displacements()
+            forces = []
+            for scell in supercells:
+                ase_atoms = AseAtoms(
+                    symbols=scell.symbols,
+                    positions=scell.positions,
+                    cell=scell.cell,
+                    pbc=True,
+                )
+                ase_atoms.calc = calculator
+                forces.append(ase_atoms.get_forces())
+
+            phonon.produce_force_constants(forces=forces)
+
+            # Post-processing to get thermal properties
+            phonon.run_mesh(mesh=mesh)
+            phonon.run_thermal_properties(t_min=0, t_step=6, t_max=294)
+            tprop_dict = phonon.get_thermal_properties_dict()
+
+            free_energies = tprop_dict["free_energy"]
+            heat_capacities = tprop_dict["heat_capacity"]
+            entropies = tprop_dict["entropy"]
+
+            all_entropies.append(entropies)
+            all_free_energies.append(free_energies)
+            all_heat_capacities.append(heat_capacities)
+
+        return (
+            np.array(all_free_energies),
+            np.array(all_heat_capacities),
+            np.array(all_entropies),
+            temperatures,
+        )
+
+    def perform_qha(
+        self,
+        volumes,
+        energies,
+        free_energies,
+        heat_capacities,
+        entropies,
+        temperatures,
+        output_dir,
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        # Debugging: print array sizes
+        print(f"Number of temperatures: {len(temperatures)}")
+        print(f"Number of free energy data points: {free_energies.shape}")
+        print(f"Number of volume data points: {len(volumes)}")
+
+        # Ensure that volumes, free_energies, and temperatures are consistent
+        if len(volumes) != len(temperatures):
+            raise ValueError(
+                "The number of volumes must match the number of temperatures"
+            )
+
+        # Initialize the QHA object
+        try:
+            qha = PhonopyQHA(
+                volumes=volumes,
+                electronic_energies=energies,
+                free_energy=free_energies,
+                cv=heat_capacities,
+                entropy=entropies,
+                temperatures=temperatures,
+                eos="murnaghan",  # or another EOS if needed
+                verbose=True,
+            )
+        except IndexError as e:
+            print(f"Error in QHA initialization: {e}")
+            raise
+
+        # Calculate thermal expansion and save plots
+        thermal_expansion_plot = os.path.join(output_dir, "thermal_expansion.png")
+        volume_temperature_plot = os.path.join(output_dir, "volume_temperature.png")
+        helmholtz_volume_plot = os.path.join(output_dir, "helmholtz_volume.png")
+
+        qha.get_thermal_expansion()
+
+        # Save thermal expansion plot
+        qha.plot_thermal_expansion()
+        plt.savefig(thermal_expansion_plot)
+
+        # Save volume-temperature plot
+        qha.plot_volume_temperature()
+        plt.savefig(volume_temperature_plot)
+
+        # Save Helmholtz free energy vs. volume plot
+        qha.plot_helmholtz_volume()
+        plt.savefig(helmholtz_volume_plot)
+
+        # Optionally save thermal expansion coefficient to a file
+        thermal_expansion_file = os.path.join(output_dir, "thermal_expansion.txt")
+        alpha = qha.write_thermal_expansion(filename=thermal_expansion_file)
+
+        return alpha
+
+    def general_melter(self, relaxed_atoms):
+        """Perform MD simulation to melt the structure, then quench it back to room temperature."""
+        self.log(f"Starting MD melting and quenching simulation for {self.jid}")
+
+        calculator = self.setup_calculator()
+        ase_atoms = relaxed_atoms.ase_converter()
+        dim = self.ensure_cell_size(ase_atoms, min_size=self.md_settings.get('min_size', 10.0))
+        supercell = relaxed_atoms.make_supercell_matrix(dim)
+        ase_atoms = supercell.ase_converter()
+        ase_atoms.calc = calculator
+
+        dt = self.md_settings.get('dt', 1) * ase.units.fs
+        temp0 = self.md_settings.get('temp0', 3500)
+        nsteps0 = self.md_settings.get('nsteps0', 1000)
+        temp1 = self.md_settings.get('temp1', 300)
+        nsteps1 = self.md_settings.get('nsteps1', 2000)
+        taut = self.md_settings.get('taut', 20) * ase.units.fs
+        trj = os.path.join(self.output_dir, f"{self.jid}_melt.traj")
+
+        # Initialize velocities and run the first part of the MD simulation
+        from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+        from ase.md.nvtberendsen import NVTBerendsen
+
+        MaxwellBoltzmannDistribution(ase_atoms, temp0 * ase.units.kB)
+        dyn = NVTBerendsen(ase_atoms, dt, temp0, taut=taut, trajectory=trj)
+
+        def myprint():
+            message = f"time={dyn.get_time() / ase.units.fs: 5.0f} fs T={ase_atoms.get_temperature(): 3.0f} K"
+            self.log(message)
+
+        dyn.attach(myprint, interval=20)
+        dyn.run(nsteps0)
+
+        # Cool down to room temperature
+        dyn.set_temperature(temp1)
+        dyn.run(nsteps1)
+
+        # Convert back to JARVIS atoms and save the final structure
+        final_atoms = ase_to_atoms(ase_atoms)
+        poscar_filename = os.path.join(
+            self.output_dir, f"POSCAR_{self.jid}_quenched_{self.calculator_type}.vasp"
+        )
+        from ase.io import write
+
+        write(poscar_filename, final_atoms.ase_converter(), format="vasp")
+        self.log(f"MD simulation completed. Final structure saved to {poscar_filename}")
+        self.job_info["quenched_atoms"] = final_atoms.to_dict()
+
+        return final_atoms
+
+    def calculate_rdf(self, quenched_atoms):
+        """Calculate Radial Distribution Function (RDF) for quenched structure and save plot."""
+        self.log(f"Starting RDF calculation for {self.jid}")
+        ase_atoms = quenched_atoms.ase_converter()
+        rmax = 3.5
+        nbins = 200
+
+        def perform_rdf_calculation(rmax):
+            from ase.ga.utilities import get_rdf
+
+            rdfs, distances = get_rdf(ase_atoms, rmax, nbins)
+            plt.figure()
+            plt.plot(distances, rdfs)
+            plt.xlabel("Distance (Å)")
+            plt.ylabel("RDF")
+            plt.title(
+                f"Radial Distribution Function for {self.jid} ({self.calculator_type})"
+            )
+            rdf_plot_filename = os.path.join(
+                self.output_dir, f"RDF_{self.jid}_{self.calculator_type}.png"
+            )
+            plt.savefig(rdf_plot_filename)
+            plt.close()
+            self.job_info["rdf_plot"] = rdf_plot_filename
+            self.log(f"RDF plot saved to {rdf_plot_filename}")
+            return rdf_plot_filename
+
+        try:
+            perform_rdf_calculation(rmax)
+        except ValueError as e:
+            if "The cell is not large enough" in str(e):
+                recommended_rmax = float(str(e).split("<")[1].split("=")[1])
+                self.log(f"Error: {e}. Adjusting rmax to {recommended_rmax}.")
+                perform_rdf_calculation(recommended_rmax)
+            else:
+                self.log(f"Error: {e}")
+                raise
+
+    def ensure_cell_size(self, ase_atoms, min_size):
+        """Ensure that all cell dimensions are at least min_size."""
+        cell_lengths = ase_atoms.get_cell().lengths()
+        scale_factors = np.ceil(min_size / cell_lengths).astype(int)
+        supercell_dims = [max(1, scale) for scale in scale_factors]
+        return supercell_dims
+
+    def analyze_interfaces(self):
+        """Perform interface analysis using intermat package."""
+        if not self.film_jid or not self.substrate_jid:
+            self.log("Film JID or substrate JID not provided, skipping interface analysis.")
+            return
+
+        self.log(f"Starting interface analysis between {self.film_jid} and {self.substrate_jid}")
+
+        # Ensure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # Prepare config
+        config = {
+            "film_jid": self.film_jid,
+            "substrate_jid": self.substrate_jid,
+            "film_index": self.film_index,
+            "substrate_index": self.substrate_index,
+            "disp_intvl": 0.05,
+            "calculator_method": self.calculator_type.lower(),
+        }
+
+        config_filename = os.path.join(
+            self.output_dir,
+            f"config_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.json"
+        )
+
+        # Save config file
+        save_dict_to_json(config, config_filename)
+        self.log(f"Config file created: {config_filename}")
+
+        # Run intermat script using subprocess in self.output_dir
+        command = f"run_intermat.py --config_file {os.path.basename(config_filename)}"
+        self.log(f"Running command: {command}")
+
+        try:
+            result = subprocess.run(
+                command,
+                shell=True,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=self.output_dir  # Set the working directory for the subprocess
+            )
+            self.log(f"Command output: {result.stdout}")
+        except subprocess.CalledProcessError as e:
+            self.log(f"Command failed with error: {e.stderr}")
+            return
+
+        # After execution, check for outputs in self.output_dir
+        main_results_filename = os.path.join(self.output_dir, "intermat_results.json")
+        if not os.path.exists(main_results_filename):
+            self.log(f"Results file not found: {main_results_filename}")
+            return
+
+        res = load_dict_from_json(main_results_filename)
+        w_adhesion = res.get("wads", [])
+        systems_info = res.get("systems", {})
+
+        # Handle intmat.png
+        intmat_filename = os.path.join(self.output_dir, "intmat.png")
+        if os.path.exists(intmat_filename):
+            new_intmat_filename = os.path.join(
+                self.output_dir,
+                f"intmat_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.png"
+            )
+            os.rename(intmat_filename, new_intmat_filename)
+            self.job_info["intmat_plot"] = new_intmat_filename
+            self.log(f"intmat.png saved as {new_intmat_filename}")
+        else:
+            self.log("intmat.png not found.")
+
+        if "wads" in res:
+            # Save additional plots or data as needed
+            self.job_info["interface_scan_results"] = main_results_filename
+            self.job_info["w_adhesion"] = w_adhesion
+            self.job_info["systems_info"] = systems_info
+            self.log(f"Interface scan results saved to {main_results_filename}")
+            self.log(f"w_adhesion: {w_adhesion}")
+            self.log(f"systems_info: {systems_info}")
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+        else:
+            self.log(f"No 'wads' key in results file: {main_results_filename}")
+
+    def get_job_info_filename(self):
+        if hasattr(self, 'jid') and self.jid:
+            return os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            )
+        else:
+            return os.path.join(
+                self.output_dir, f"Interface_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}_job_info.json"
+            )
+
+    import numpy as np
+    import pandas as pd
+    from sklearn.metrics import mean_absolute_error, r2_score
+    import matplotlib.pyplot as plt
+    from matplotlib.gridspec import GridSpec
+    import os
+
+    def run_all(self):
+        """Run selected analyses based on configuration."""
+        # Start timing the entire run
+        start_time = time.time()
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for analysis.")
+            self.atoms = self.atoms.get_conventional_atoms
+        else:
+            self.atoms = self.atoms
+        # Relax the structure if specified
+        if 'relax_structure' in self.properties_to_calculate:
+            relaxed_atoms = self.relax_structure()
+        else:
+            relaxed_atoms = self.atoms
+
+        # Proceed only if the structure is relaxed or original atoms are used
+        if relaxed_atoms is None:
+            self.log("Relaxation did not converge. Exiting.")
+            return
+
+        if 'calculate_forces' in self.properties_to_calculate:
+            self.calculate_forces(self.atoms)
+
+        # Lattice parameters before and after relaxation
+        lattice_initial = self.atoms.lattice
+        lattice_final = relaxed_atoms.lattice
+
+        # Prepare final results dictionary
+        final_results = {}
+
+        # Initialize variables for error calculation
+        err_a = err_b = err_c = err_vol = err_form = err_kv = err_c11 = err_c44 = err_surf_en = err_vac_en = np.nan
+        form_en_entry = kv_entry = c11_entry = c44_entry = 0
+
+        # Calculate E-V curve and bulk modulus if specified
+        if 'calculate_ev_curve' in self.properties_to_calculate:
+            _, _, _, _, bulk_modulus, _, _ = self.calculate_ev_curve(relaxed_atoms)
+            kv_entry = self.reference_data.get("bulk_modulus_kv", 0)
+            final_results['modulus'] = {
+                'kv': bulk_modulus,
+                'kv_entry': kv_entry
+            }
+            err_kv = mean_absolute_error([kv_entry], [bulk_modulus]) if bulk_modulus is not None else np.nan
+
+                
+        # Formation energy
+        if 'calculate_formation_energy' in self.properties_to_calculate:
+            formation_energy = self.calculate_formation_energy(relaxed_atoms)
+            form_en_entry = self.reference_data.get("formation_energy_peratom", 0)
+            final_results['form_en'] = {
+                'form_energy': formation_energy,
+                'form_energy_entry': form_en_entry
+            }
+            err_form = mean_absolute_error([form_en_entry], [formation_energy])
+
+        # Elastic tensor
+        if 'calculate_elastic_tensor' in self.properties_to_calculate:
+            elastic_tensor = self.calculate_elastic_tensor(relaxed_atoms)
+            c11_entry = self.reference_data.get("elastic_tensor", [[0]])[0][0]
+            c44_entry = self.reference_data.get("elastic_tensor", [[0, 0, 0, [0, 0, 0, 0]]])[3][3]
+            final_results['elastic_tensor'] = {
+                'c11': elastic_tensor.get("C_11", 0),
+                'c44': elastic_tensor.get("C_44", 0),
+                'c11_entry': c11_entry,
+                'c44_entry': c44_entry
+            }
+            err_c11 = mean_absolute_error([c11_entry], [elastic_tensor.get("C_11", np.nan)])
+            err_c44 = mean_absolute_error([c44_entry], [elastic_tensor.get("C_44", np.nan)])
+
+        # Phonon analysis
+        if 'run_phonon_analysis' in self.properties_to_calculate:
+            phonon, zpe = self.run_phonon_analysis(relaxed_atoms)
+            final_results['zpe'] = zpe
+        else:
+            zpe = None
+
+        # Surface energy analysis
+        if 'analyze_surfaces' in self.properties_to_calculate:
+            self.analyze_surfaces()
+            surf_en, surf_en_entry = [], []
+            surface_entries = get_surface_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+
+            indices_list = self.surface_settings.get('indices_list', [
+                [1, 0, 0],
+                [1, 1, 1],
+                [1, 1, 0],
+                [0, 1, 1],
+                [0, 0, 1],
+                [0, 1, 0],
+            ])
+
+            for indices in indices_list:
+                surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+                calculated_surface_energy = self.job_info.get(surface_name, 0)
+                try:
+                    # Try to match the surface entry
+                    matching_entry = next(
+                        (entry for entry in surface_entries if entry['name'].strip() == surface_name.strip()),
+                        None
+                    )
+                    if matching_entry and calculated_surface_energy != 0 and matching_entry["surf_en_entry"] != 0:
+                        surf_en.append(calculated_surface_energy)
+                        surf_en_entry.append(matching_entry["surf_en_entry"])
+                    else:
+                        print(f"No valid matching entry found for {surface_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing surface {surface_name}: {e}")
+                    self.log(f"Error processing surface {surface_name}: {str(e)}")
+                    continue  # Skip this surface and move to the next one
+            final_results['surface_energy'] = [
+                {
+                    "name": f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}",
+                    "surf_en": se,
+                    "surf_en_entry": see
+                }
+                for se, see, indices in zip(surf_en, surf_en_entry, indices_list)
+            ]
+            err_surf_en = mean_absolute_error(surf_en_entry, surf_en) if surf_en else np.nan
+
+        # Vacancy energy analysis
+        if 'analyze_defects' in self.properties_to_calculate:
+            self.analyze_defects()
+            vac_en, vac_en_entry = [], []
+            vacancy_entries = get_vacancy_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+            for defect in Vacancy(self.atoms).generate_defects(on_conventional_cell=True, enforce_c_size=8, extend=1):
+                defect_name = f"{self.jid}_{defect.to_dict()['symbol']}"
+                vacancy_energy = self.job_info.get(f"vacancy_formation_energy for {defect_name}", 0)
+                try:
+                    # Try to match the vacancy entry
+                    matching_entry = next(
+                        (entry for entry in vacancy_entries if entry['symbol'] == defect_name),
+                        None
+                    )
+                    if matching_entry and vacancy_energy != 0 and matching_entry['vac_en_entry'] != 0:
+                        vac_en.append(vacancy_energy)
+                        vac_en_entry.append(matching_entry['vac_en_entry'])
+                    else:
+                        print(f"No valid matching entry found for {defect_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing defect {defect_name}: {e}")
+                    self.log(f"Error processing defect {defect_name}: {str(e)}")
+                    continue  # Skip this defect and move to the next one
+            final_results['vacancy_energy'] = [
+                {"name": ve_name, "vac_en": ve, "vac_en_entry": vee}
+                for ve_name, ve, vee in zip(
+                    [f"{self.jid}_{defect.to_dict()['symbol']}" for defect in Vacancy(self.atoms).generate_defects(
+                        on_conventional_cell=True, enforce_c_size=8, extend=1
+                    )],
+                    vac_en,
+                    vac_en_entry
+                )
+            ]
+            err_vac_en = mean_absolute_error(vac_en_entry, vac_en) if vac_en else np.nan
+
+        # Additional analyses
+        if 'analyze_interfaces' in self.properties_to_calculate and self.film_jid and self.substrate_jid:
+            self.analyze_interfaces()
+
+        if 'run_phonon3_analysis' in self.properties_to_calculate:
+            self.run_phonon3_analysis(relaxed_atoms)
+
+        if 'calculate_thermal_expansion' in self.properties_to_calculate:
+            self.calculate_thermal_expansion(relaxed_atoms)
+
+        if 'general_melter' in self.properties_to_calculate:
+            quenched_atoms = self.general_melter(relaxed_atoms)
+            if 'calculate_rdf' in self.properties_to_calculate:
+                self.calculate_rdf(quenched_atoms)
+
+        # Record lattice parameters
+        final_results['energy'] = {
+            "initial_a": lattice_initial.a,
+            "initial_b": lattice_initial.b,
+            "initial_c": lattice_initial.c,
+            "initial_vol": lattice_initial.volume,
+            "final_a": lattice_final.a,
+            "final_b": lattice_final.b,
+            "final_c": lattice_final.c,
+            "final_vol": lattice_final.volume,
+            "energy": self.job_info.get("final_energy_structure", 0)
+        }
+
+        # Error calculations
+        err_a = mean_absolute_error([lattice_initial.a], [lattice_final.a])
+        err_b = mean_absolute_error([lattice_initial.b], [lattice_final.b])
+        err_c = mean_absolute_error([lattice_initial.c], [lattice_final.c])
+        err_vol = mean_absolute_error([lattice_initial.volume], [lattice_final.volume])
+
+        # Create an error dictionary
+        error_dat = {
+            "err_a": err_a,
+            "err_b": err_b,
+            "err_c": err_c,
+            "err_form": err_form,
+            "err_vol": err_vol,
+            "err_kv": err_kv,
+            "err_c11": err_c11,
+            "err_c44": err_c44,
+            "err_surf_en": err_surf_en,
+            "err_vac_en": err_vac_en,
+            "time": time.time() - start_time
+        }
+
+        print("Error metrics calculated:", error_dat)
+
+        # Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+        # Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+        # Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        # Write results to a JSON file
+        output_file = os.path.join(self.output_dir, f"{self.jid}_{self.calculator_type}_results.json")
+        save_dict_to_json(final_results, output_file)
+
+        # Log total time
+        total_time = error_dat['time']
+        self.log(f"Total time for run: {total_time} seconds")
+
+        return error_dat
+
+
+# Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+# Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+# Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        return error_dat
+
+    def plot_error_scorecard(self, df):
+        import plotly.express as px
+
+        fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+        unique_dir = os.path.basename(self.output_dir)
+        fname_plot = os.path.join(self.output_dir, f"{unique_dir}_error_scorecard.png")
+        fig.write_image(fname_plot)
+        fig.show()
+
+def analyze_multiple_structures(jid_list, calculator_types, chemical_potentials_file):
+    composite_error_data = {}
+
+    for calculator_type in calculator_types:
+        # List to store individual error DataFrames
+        error_dfs = []
+
+        for jid in jid_list:
+            print(f"Analyzing {jid} with {calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                jid=jid,
+                calculator_type=calculator_type,
+                chemical_potentials_file=chemical_potentials_file,
+            )
+            # Run analysis and get error data
+            error_dat = analyzer.run_all()
+            error_df = pd.DataFrame([error_dat])
+            error_dfs.append(error_df)
+
+        # Concatenate all error DataFrames
+        all_errors_df = pd.concat(error_dfs, ignore_index=True)
+
+        # Compute composite errors by ignoring NaN values
+        composite_error = all_errors_df.mean(skipna=True).to_dict()
+
+        # Store the composite error data for this calculator type
+        composite_error_data[calculator_type] = composite_error
+
+    # Once all materials and calculators have been processed, create a DataFrame
+    composite_df = pd.DataFrame(composite_error_data).transpose()
+
+    # Plot the composite scorecard
+    plot_composite_scorecard(composite_df)
+
+    # Save the composite dataframe
+    composite_df.to_csv("composite_error_data.csv", index=True)
+
+def analyze_multiple_interfaces(film_jid_list, substrate_jid_list, calculator_types, chemical_potentials_file, film_index="1_1_0", substrate_index="1_1_0"):
+    for calculator_type in calculator_types:
+        for film_jid in film_jid_list:
+            for substrate_jid in substrate_jid_list:
+                print(f"Analyzing interface between {film_jid} and {substrate_jid} with {calculator_type}...")
+                analyzer = MaterialsAnalyzer(
+                    calculator_type=calculator_type,
+                    chemical_potentials_file=chemical_potentials_file,
+                    film_jid=film_jid,
+                    substrate_jid=substrate_jid,
+                    film_index=film_index,
+                    substrate_index=substrate_index,
+                )
+                analyzer.analyze_interfaces()
+
+def plot_composite_scorecard(df):
+    """Plot the composite scorecard for all calculators"""
+    fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+    fig.update_layout(title="Composite Scorecard for Calculators")
+    
+    # Save plot
+    fname_plot = "composite_error_scorecard.png"
+    fig.write_image(fname_plot)
+    fig.show()
+
+
+class MLearnForcesAnalyzer:
+    def __init__(self, calculator_type, mlearn_elements, output_dir=None):
+        self.calculator_type = calculator_type
+        self.mlearn_elements = mlearn_elements
+        elements_str = "_".join(self.mlearn_elements)
+        self.output_dir = output_dir or f"mlearn_analysis_{elements_str}_{calculator_type}"
+        os.makedirs(self.output_dir, exist_ok=True)
+        self.log_file = os.path.join(self.output_dir, "mlearn_analysis_log.txt")
+        self.setup_logger()
+        self.calculator = self.setup_calculator()
+        self.job_info = {
+            "calculator_type": calculator_type,
+            "mlearn_elements": mlearn_elements,
+        }
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger("MLearnForcesAnalyzer")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        self.logger.info(message)
+        print(message)
+
+    def setup_calculator(self):
+        return setup_calculator(self.calculator_type)
+
+    def run(self):
+        for element in self.mlearn_elements:
+            self.compare_mlearn_properties(element)
+
+    def compare_mlearn_properties(self, element):
+        """
+        Compare forces and stresses calculated by the FF calculator with mlearn DFT data for a given element.
+
+        Args:
+            element (str): Element symbol to filter structures (e.g., 'Si').
+        """
+        # Download the mlearn dataset if not already present
+        mlearn_zip_path = 'mlearn.json.zip'
+        if not os.path.isfile(mlearn_zip_path):
+            self.log("Downloading mlearn dataset...")
+            url = 'https://figshare.com/ndownloader/files/40357663'
+            response = requests.get(url)
+            with open(mlearn_zip_path, 'wb') as f:
+                f.write(response.content)
+            self.log("Download completed.")
+
+        # Read the JSON data from the zip file
+        with zipfile.ZipFile(mlearn_zip_path, 'r') as z:
+            with z.open('mlearn.json') as f:
+                mlearn_data = json.load(f)
+
+        # Convert mlearn data to DataFrame
+        df = pd.DataFrame(mlearn_data)
+
+        # Filter the dataset for the specified element
+        df['elements'] = df['atoms'].apply(lambda x: x['elements'])
+        df = df[df['elements'].apply(lambda x: element in x)]
+        df = df.reset_index(drop=True)
+        self.log(f"Filtered dataset to {len(df)} entries containing element '{element}'")
+
+        # Initialize lists to store results
+        force_results = []
+        stress_results = []
+
+        # Iterate over each structure
+        for idx, row in df.iterrows():
+            jid = row.get('jid', f"structure_{idx}")
+            atoms_dict = row['atoms']
+            atoms = Atoms.from_dict(atoms_dict)
+            dft_forces = np.array(row['forces'])
+            dft_stresses = np.array(row['stresses'])  # Original stresses in kBar
+
+            # Convert DFT stresses from kBar to GPa
+            dft_stresses_GPa = dft_stresses * -0.1  # kBar to GPa
+
+            # Convert DFT stresses to full 3x3 tensors
+            if dft_stresses_GPa.ndim == 1 and dft_stresses_GPa.size == 6:
+                dft_stress_tensor = voigt_6_to_full_3x3_stress(dft_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: DFT stresses not in expected format.")
+                continue  # Skip structures with unexpected stress format
+
+            # Calculate predicted properties
+            predicted_forces, predicted_stresses = self.calculate_properties(atoms)
+
+            # Convert predicted stresses from eV/ų to GPa
+            if predicted_stresses is not None and predicted_stresses.size == 6:
+                predicted_stresses_GPa = predicted_stresses * 160.21766208  # eV/ų to GPa
+                predicted_stress_tensor = voigt_6_to_full_3x3_stress(predicted_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: Predicted stresses not available.")
+                continue  # Skip structures where stresses are not available
+
+            # Flatten the 3x3 stress tensors to 9-component arrays for comparison
+            dft_stress_flat = dft_stress_tensor.flatten()
+            predicted_stress_flat = predicted_stress_tensor.flatten()
+
+            # Store the results
+            force_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_forces.flatten())),
+                'prediction': ';'.join(map(str, predicted_forces.flatten())),
+            })
+            stress_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_stress_flat)),
+                'prediction': ';'.join(map(str, predicted_stress_flat)),
+            })
+
+            # Optional: Progress indicator
+            if idx % 10 == 0:
+                self.log(f"Processed {idx + 1}/{len(df)} structures.")
+
+        # Ensure we have data to process
+        if not force_results or not stress_results:
+            self.log("No valid data found for forces or stresses. Exiting.")
+            return
+
+        # Save results to CSV files
+        force_df = pd.DataFrame(force_results)
+        force_csv = os.path.join(self.output_dir, f'AI-MLFF-forces-mlearn_{element}-test-multimae.csv')
+        force_df.to_csv(force_csv, index=False)
+        self.log(f"Saved force comparison data to '{force_csv}'")
+
+        stress_df = pd.DataFrame(stress_results)
+        stress_csv = os.path.join(self.output_dir, f'AI-MLFF-stresses-mlearn_{element}-test-multimae.csv')
+        stress_df.to_csv(stress_csv, index=False)
+        self.log(f"Saved stress comparison data to '{stress_csv}'")
+
+        # Zip the CSV files
+        self.zip_file(force_csv)
+        self.zip_file(stress_csv)
+
+        # Calculate error metrics
+        # Forces MAE
+        target_forces = np.concatenate(force_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_forces = np.concatenate(force_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        forces_mae = mean_absolute_error(target_forces, pred_forces)
+        self.log(f"Forces MAE for element '{element}': {forces_mae:.6f} eV/Å")
+
+        # Stresses MAE
+        target_stresses = np.concatenate(stress_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_stresses = np.concatenate(stress_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        stresses_mae = mean_absolute_error(target_stresses, pred_stresses)
+        self.log(f"Stresses MAE for element '{element}': {stresses_mae:.6f} GPa")
+
+        # Save MAE to job_info
+        self.job_info[f'forces_mae_{element}'] = forces_mae
+        self.job_info[f'stresses_mae_{element}'] = stresses_mae
+        self.save_job_info()
+
+        # Plot parity plots
+        forces_plot_filename = os.path.join(self.output_dir, f'forces_parity_plot_{element}.png')
+        self.plot_parity(target_forces, pred_forces, 'Forces', 'eV/Å', forces_plot_filename, element)
+
+        stresses_plot_filename = os.path.join(self.output_dir, f'stresses_parity_plot_{element}.png')
+        self.plot_parity(target_stresses, pred_stresses, 'Stresses', 'GPa', stresses_plot_filename, element)
+
+    def calculate_properties(self, atoms):
+        """
+        Calculate forces and stresses on the given atoms.
+
+        Returns:
+            Tuple of forces and stresses.
+        """
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Calculate properties
+        forces = ase_atoms.get_forces()
+        stresses = ase_atoms.get_stress()  # Voigt 6-component stress
+
+        return forces, stresses  # Return forces and stresses in Voigt notation
+
+    def plot_parity(self, target, prediction, property_name, units, filename, element):
+        """
+        Plot parity plot for a given property.
+
+        Args:
+            target (array-like): Target values.
+            prediction (array-like): Predicted values.
+            property_name (str): Name of the property (e.g., 'Forces').
+            units (str): Units of the property (e.g., 'eV/Å' or 'GPa').
+            filename (str): Filename to save the plot.
+            element (str): Element symbol.
+        """
+        plt.figure(figsize=(8, 8), dpi=300)
+        plt.scatter(target, prediction, alpha=0.5, edgecolors='k', s=20)
+        min_val = min(np.min(target), np.min(prediction))
+        max_val = max(np.max(target), np.max(prediction))
+        plt.plot([min_val, max_val], [min_val, max_val], 'k--', lw=2)
+        plt.xlabel(f'Target {property_name} ({units})', fontsize=14)
+        plt.ylabel(f'Predicted {property_name} ({units})', fontsize=14)
+        plt.title(f'Parity Plot for {property_name} - Element {element}', fontsize=16)
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(filename)
+        plt.close()
+        self.log(f"Saved parity plot for {property_name} as '{filename}'")
+
+    def zip_file(self, filename):
+        zip_filename = filename + '.zip'
+        with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
+            zf.write(filename, arcname=os.path.basename(filename))
+        os.remove(filename)  # Remove the original file
+        self.log(f"Zipped data to '{zip_filename}'")
+
+    def save_job_info(self):
+        job_info_filename = os.path.join(self.output_dir, f"mlearn_{self.calculator_type}_job_info.json")
+        with open(job_info_filename, 'w') as f:
+            json.dump(self.job_info, f, indent=4)
+
+
+#jid_list=['JVASP-1002']
+jid_list_all = [ 'JVASP-1002', 'JVASP-816', 'JVASP-867', 'JVASP-1029', 'JVASP-861','JVASP-30', 'JVASP-8169', 'JVASP-890', 'JVASP-8158','JVASP-8118',
+    'JVASP-107', 'JVASP-39', 'JVASP-7844', 'JVASP-35106', 'JVASP-1174',
+    'JVASP-1372', 'JVASP-91', 'JVASP-1186', 'JVASP-1408', 'JVASP-105410',
+    'JVASP-1177', 'JVASP-79204', 'JVASP-1393', 'JVASP-1312', 'JVASP-1327',
+    'JVASP-1183', 'JVASP-1192', 'JVASP-8003', 'JVASP-96', 'JVASP-1198',
+    'JVASP-1195', 'JVASP-9147', 'JVASP-41', 'JVASP-34674', 'JVASP-113',
+    'JVASP-32', 'JVASP-840', 'JVASP-21195', 'JVASP-981', 'JVASP-969',
+    'JVASP-802', 'JVASP-943', 'JVASP-14812', 'JVASP-984', 'JVASP-972',
+    'JVASP-958', 'JVASP-901', 'JVASP-1702', 'JVASP-931', 'JVASP-963',
+    'JVASP-95', 'JVASP-1201', 'JVASP-14837', 'JVASP-825', 'JVASP-966',
+    'JVASP-993', 'JVASP-23', 'JVASP-828', 'JVASP-1189', 'JVASP-810',
+    'JVASP-7630', 'JVASP-819', 'JVASP-1180', 'JVASP-837', 'JVASP-919',
+    'JVASP-7762', 'JVASP-934', 'JVASP-858', 'JVASP-895']
+#calculator_types = ["alignn_ff_aff307k_lmdb_param_low_rad_use_force_mult_mp_tak4","alignn_ff_v5.27.2024","alignn_ff_aff307k_kNN_2_2_128"]
+
+if __name__ == "__main__":
+    import pprint
+    parser = argparse.ArgumentParser(description="Run Materials Analyzer")
+    parser.add_argument("--input_file", default="input.json", type=str, help="Path to the input configuration JSON file")
+    args = parser.parse_args()
+
+    input_file = loadjson(args.input_file)
+    input_file_data = CHIPSFFConfig(**input_file)
+    pprint.pprint(input_file_data.dict())
+
+    # If film_id is provided, treat it as a list
+    film_jids = input_file_data.film_id if input_file_data.film_id else []
+
+    # If substrate_id is provided, treat it as a list
+    substrate_jids = input_file_data.substrate_id if input_file_data.substrate_id else []
+
+    # Case 1: Interface calculations with film_jid and substrate_jid
+    if film_jids and substrate_jids:
+        # Loop through all film and substrate JIDs and perform interface analysis
+        for film_jid, substrate_jid in zip(film_jids, substrate_jids):
+            print(f"Analyzing interface between {film_jid} and {substrate_jid} with {input_file_data.calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                calculator_type=input_file_data.calculator_type,
+                chemical_potentials_file=input_file_data.chemical_potentials_file,
+                film_jid=film_jid,
+                substrate_jid=substrate_jid,
+                film_index=input_file_data.film_index,
+                substrate_index=input_file_data.substrate_index,
+                bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+                phonon_settings=input_file_data.phonon_settings,
+                properties_to_calculate=input_file_data.properties_to_calculate,
+            )
+            analyzer.analyze_interfaces()
+
+    # Case 2: Single JID provided
+    elif input_file_data.jid and input_file_data.calculator_type:
+        print(f"Analyzing {input_file_data.jid} with {input_file_data.calculator_type}...")
+        analyzer = MaterialsAnalyzer(
+            jid=input_file_data.jid,
+            calculator_type=input_file_data.calculator_type,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+        analyzer.run_all()
+
+    # Case 3: Multiple JIDs and calculator types provided (batch processing)
+    elif input_file_data.jid_list and input_file_data.calculator_types:
+        analyze_multiple_structures(
+            jid_list=input_file_data.jid_list,
+            calculator_types=input_file_data.calculator_types,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+
+    elif input_file_data.mlearn_elements and input_file_data.calculator_type:
+        print(f"Running mlearn forces comparison for elements {input_file_data.mlearn_elements} with {input_file_data.calculator_type}...")
+        mlearn_analyzer = MLearnForcesAnalyzer(
+            calculator_type=input_file_data.calculator_type,
+            mlearn_elements=input_file_data.mlearn_elements
+        )
+        mlearn_analyzer.run()
+    else:
+        print("Please provide valid arguments in the configuration file.")
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/AI-MLFF-forces-mlearnall_Ge-test-multimae.csv.zip b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Ge-test-multimae.csv.zip
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/AI-MLFF-forces-mlearnall_Ge-test-multimae.csv.zip
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Ge-test-multimae.csv.zip
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/metadata.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/metadata.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/metadata.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/mlearn_alignn_ff_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/mlearn_alignn_ff_job_info.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/mlearn_analysis_log.txt
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/mlearn_analysis_log.txt
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/mlearn_analysis_log.txt
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/run.sh
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/run.sh
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/run.sh
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/run_chipsff.py
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff/run_chipsff.py
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ge_alignn_ff_5_27_24/run_chipsff.py
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/.DS_Store b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/.DS_Store
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literal 0
HcmV?d00001

diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/metadata.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/metadata.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/metadata.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
new file mode 100644
index 000000000..a700ee057
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
@@ -0,0 +1,8 @@
+{
+    "calculator_type": "alignn_ff_12_2_24",
+    "mlearn_elements": [
+        "Li"
+    ],
+    "forces_mae_Li": 0.12188066042696061,
+    "stresses_mae_Li": 0.43449990685579476
+}
\ No newline at end of file
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/mlearn_analysis_log.txt
new file mode 100644
index 000000000..4f985e248
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/mlearn_analysis_log.txt
@@ -0,0 +1,36 @@
+2024-12-03 16:34:59,871 - INFO - Filtered dataset to 270 entries containing element 'Li'
+2024-12-03 16:35:07,046 - INFO - Processed 1/270 structures.
+2024-12-03 16:35:10,326 - INFO - Processed 11/270 structures.
+2024-12-03 16:35:13,532 - INFO - Processed 21/270 structures.
+2024-12-03 16:35:16,781 - INFO - Processed 31/270 structures.
+2024-12-03 16:35:20,042 - INFO - Processed 41/270 structures.
+2024-12-03 16:35:23,305 - INFO - Processed 51/270 structures.
+2024-12-03 16:35:26,229 - INFO - Processed 61/270 structures.
+2024-12-03 16:35:29,758 - INFO - Processed 71/270 structures.
+2024-12-03 16:35:33,174 - INFO - Processed 81/270 structures.
+2024-12-03 16:35:36,574 - INFO - Processed 91/270 structures.
+2024-12-03 16:35:40,080 - INFO - Processed 101/270 structures.
+2024-12-03 16:35:43,827 - INFO - Processed 111/270 structures.
+2024-12-03 16:35:47,521 - INFO - Processed 121/270 structures.
+2024-12-03 16:35:51,043 - INFO - Processed 131/270 structures.
+2024-12-03 16:35:54,665 - INFO - Processed 141/270 structures.
+2024-12-03 16:35:58,204 - INFO - Processed 151/270 structures.
+2024-12-03 16:36:01,798 - INFO - Processed 161/270 structures.
+2024-12-03 16:36:05,497 - INFO - Processed 171/270 structures.
+2024-12-03 16:36:09,432 - INFO - Processed 181/270 structures.
+2024-12-03 16:36:13,116 - INFO - Processed 191/270 structures.
+2024-12-03 16:36:16,916 - INFO - Processed 201/270 structures.
+2024-12-03 16:36:19,962 - INFO - Processed 211/270 structures.
+2024-12-03 16:36:21,998 - INFO - Processed 221/270 structures.
+2024-12-03 16:36:22,667 - INFO - Processed 231/270 structures.
+2024-12-03 16:36:23,333 - INFO - Processed 241/270 structures.
+2024-12-03 16:36:26,637 - INFO - Processed 251/270 structures.
+2024-12-03 16:36:29,621 - INFO - Processed 261/270 structures.
+2024-12-03 16:36:31,318 - INFO - Saved force comparison data to 'mlearn_analysis_Li_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Li-test-multimae.csv'
+2024-12-03 16:36:31,327 - INFO - Saved stress comparison data to 'mlearn_analysis_Li_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Li-test-multimae.csv'
+2024-12-03 16:36:31,472 - INFO - Zipped data to 'mlearn_analysis_Li_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Li-test-multimae.csv.zip'
+2024-12-03 16:36:31,485 - INFO - Zipped data to 'mlearn_analysis_Li_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Li-test-multimae.csv.zip'
+2024-12-03 16:36:31,520 - INFO - Forces MAE for element 'Li': 0.121881 eV/Å
+2024-12-03 16:36:31,528 - INFO - Stresses MAE for element 'Li': 0.434500 GPa
+2024-12-03 16:36:33,844 - INFO - Saved parity plot for Forces as 'mlearn_analysis_Li_alignn_ff_12_2_24/forces_parity_plot_Li.png'
+2024-12-03 16:36:34,834 - INFO - Saved parity plot for Stresses as 'mlearn_analysis_Li_alignn_ff_12_2_24/stresses_parity_plot_Li.png'
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/run.sh
new file mode 100644
index 000000000..0e331ca03
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/run.sh
@@ -0,0 +1,148 @@
+#!/bin/bash
+
+# Create logs directory if it doesn't exist
+mkdir -p logs
+
+# Define arrays of JIDs, calculators, and mlearn elements
+calculator_types=("alignn_ff_12_2_24")
+mlearn_elements=("Si" "Ge" "Cu" "Ni" "Li" "Mo")
+
+# Function to submit a job for bulk material calculations
+submit_bulk_job() {
+  local jid=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=30-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=${jid}_${calculator}
+#SBATCH --output=logs/${jid}_${calculator}_%j.out
+#SBATCH --error=logs/${jid}_${calculator}_%j.err
+
+# Generate input JSON file for this combination
+cat > input_${jid}_${calculator}.json <<JSON
+{
+  "jid": "$jid",
+  "calculator_type": "$calculator",
+  "chemical_potentials_file": "chemical_potentials.json",
+  "properties_to_calculate": [
+    "relax_structure",
+    "calculate_ev_curve",
+    "calculate_formation_energy",
+    "calculate_elastic_tensor",
+    "run_phonon_analysis",
+    "analyze_surfaces",
+    "analyze_defects",
+    "run_phonon3_analysis",
+    "general_melter",
+    "calculate_rdf"
+  ],
+  "bulk_relaxation_settings": {
+    "filter_type": "ExpCellFilter",
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": false
+    }
+  },
+  "phonon_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "use_conventional_cell": true,
+  "surface_settings": {
+    "indices_list": [
+      [0, 1, 0],
+      [0, 0, 1]
+    ],
+    "layers": 4,
+    "vacuum": 18,
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "defect_settings": {
+    "generate_settings": {
+      "on_conventional_cell": true,
+      "enforce_c_size": 8,
+      "extend": 1
+    },
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "phonon3_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "md_settings": {
+    "dt": 1,
+    "temp0": 35,
+    "nsteps0": 10,
+    "temp1": 200,
+    "nsteps1": 20,
+    "taut": 20,
+    "min_size": 10.0
+  }
+}
+JSON
+
+# Run the Python analysis for this JID/calculator combination
+python run_chipsff.py --input_file input_${jid}_${calculator}.json
+
+EOT
+}
+
+# Function to submit a job for mlearn analysis
+submit_mlearn_job() {
+  local element=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=1-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=mlearn_${element}_${calculator}
+#SBATCH --output=logs/mlearn_${element}_${calculator}_%j.out
+#SBATCH --error=logs/mlearn_${element}_${calculator}_%j.err
+
+# Generate input JSON file for this mlearn element/calculator combination
+cat > input_mlearn_${element}_${calculator}.json <<JSON
+{
+  "mlearn_elements": ["$element"],
+  "calculator_type": "$calculator",
+  "properties_to_calculate": ["compare_mlearn_forces"]
+}
+JSON
+
+# Run the Python analysis for this mlearn element/calculator combination
+python run_chipsff.py --input_file input_mlearn_${element}_${calculator}.json
+
+EOT
+}
+
+# Loop through each JID and calculator combination for bulk calculations
+for jid in "${jid_list[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_bulk_job "$jid" "$calculator"
+  done
+done
+
+# Loop through each mlearn element and calculator combination for mlearn analysis
+for element in "${mlearn_elements[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_mlearn_job "$element" "$calculator"
+  done
+done
+
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/run_chipsff.py
new file mode 100644
index 000000000..79b65eb07
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_12_2_24/run_chipsff.py
@@ -0,0 +1,2286 @@
+#!/usr/bin/env python
+import os
+import subprocess
+import sys
+import time
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from ase.eos import EquationOfState
+from ase import Atoms as AseAtoms
+from ase.units import kJ
+from ase.constraints import ExpCellFilter
+from ase.optimize.fire import FIRE
+from ase.stress import voigt_6_to_full_3x3_stress
+import ase.units
+from jarvis.db.figshare import get_jid_data, data, get_request_data
+from jarvis.core.atoms import Atoms, ase_to_atoms
+from jarvis.io.vasp.inputs import Poscar
+from jarvis.core.kpoints import Kpoints3D as Kpoints
+from jarvis.analysis.defects.vacancy import Vacancy
+from jarvis.analysis.defects.surface import Surface
+import pandas as pd
+import h5py
+import shutil
+import glob
+import io
+import contextlib
+import requests
+import zipfile
+import re
+import plotly.express as px
+from sklearn.metrics import mean_absolute_error, r2_score
+from matplotlib.gridspec import GridSpec
+import argparse
+from jarvis.db.jsonutils import loadjson
+from chipsff.config import CHIPSFFConfig
+
+dft_3d = data("dft_3d")
+vacancydb = data("vacancydb")
+surf_url = "https://figshare.com/ndownloader/files/46355689"
+surface_data = get_request_data(js_tag="surface_db_dd.json", url=surf_url)
+
+def get_entry(jid):
+    for entry in dft_3d:
+        if entry["jid"] == jid:
+            return entry
+    raise ValueError(f"JID {jid} not found in the database")
+
+def collect_data(dft_3d, vacancydb, surface_data):
+    defect_ids = list(set([entry["jid"] for entry in vacancydb]))
+    surf_ids = list(set([entry["name"].split("Surface-")[1].split("_miller_")[0] for entry in surface_data]))
+
+    aggregated_data = []
+    for entry in dft_3d:
+        tmp = entry
+        tmp["vacancy"] = {}
+        tmp["surface"] = {}
+
+        # Check if the entry is in the defect dataset
+        if entry["jid"] in defect_ids:
+            for vac_entry in vacancydb:
+                if entry["jid"] == vac_entry["jid"]:
+                    tmp["vacancy"].setdefault(
+                        vac_entry["id"].split("_")[0] + "_" + vac_entry["id"].split("_")[1],
+                        vac_entry["ef"]
+                    )
+
+        # Check if the entry is in the surface dataset
+        if entry["jid"] in surf_ids:
+            for surf_entry in surface_data:
+                jid = surf_entry["name"].split("Surface-")[1].split("_miller_")[0]
+                if entry["jid"] == jid:
+                    tmp["surface"].setdefault(
+                        "_".join(surf_entry["name"].split("_thickness")[0].split("_")[0:5]),
+                        surf_entry["surf_en"]
+                    )
+
+        aggregated_data.append(tmp)
+    
+    return aggregated_data
+
+def get_vacancy_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the vacancy formation energy entry (vac_en_entry) for a given jid.
+    
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+    
+    Returns:
+    dict: A dictionary containing the vacancy formation energy entry and corresponding symbol.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            vacancy_data = entry.get("vacancy", {})
+            if vacancy_data:
+                return [{"symbol": key, "vac_en_entry": value} for key, value in vacancy_data.items()]
+            else:
+                return f"No vacancy data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+    
+def get_surface_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the surface energy entry (surf_en_entry) for a given jid.
+
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+
+    Returns:
+    list: A list of dictionaries containing the surface energy entry and corresponding name.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            surface_data = entry.get("surface", {})
+            if surface_data:
+                # Prepend 'Surface-JVASP-<jid>_' to the key for correct matching
+                return [{"name": f"{key}", "surf_en_entry": value} for key, value in surface_data.items()]
+            else:
+                return f"No surface data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+
+
+def log_job_info(message, log_file):
+    """Log job information to a file and print it."""
+    with open(log_file, "a") as f:
+        f.write(message + "\n")
+    print(message)
+
+def save_dict_to_json(data_dict, filename):
+    with open(filename, "w") as f:
+        json.dump(data_dict, f, indent=4)
+
+def load_dict_from_json(filename):
+    with open(filename, 'r') as f:
+        return json.load(f)
+        
+def setup_calculator(calculator_type):
+    if calculator_type == "matgl":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "matgl-direct":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-DIRECT-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "alignn_ff":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+        model_path = default_path() #can be adjusted to other ALIGNN models
+        return AlignnAtomwiseCalculator(
+            path=model_path,
+            stress_wt=0.1,
+            force_mult_natoms=False,
+            force_multiplier=1,
+            modl_filename="best_model.pt",
+        )
+    elif calculator_type == "alignn_ff_12_2_24":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+        return AlignnAtomwiseCalculator()
+    elif calculator_type == "chgnet":
+        from chgnet.model.dynamics import CHGNetCalculator
+        return CHGNetCalculator()
+    elif calculator_type == "mace":
+        from mace.calculators import mace_mp
+        return mace_mp()
+    elif calculator_type == "mace-alexandria":
+        from mace.calculators.mace import MACECalculator
+        model_path="/users/dtw2/utils/models/alexandria_v2/mace/2D_universal_force_field_cpu.model" #adjust path to mace-alexandria
+        return MACECalculator(model_path,device="cpu") 
+    elif calculator_type == "sevennet":
+        from sevenn.sevennet_calculator import SevenNetCalculator
+        checkpoint_path = "/users/dtw2/SevenNet/pretrained_potentials/SevenNet_0__11July2024/checkpoint_sevennet_0.pth" #adjust path to sevennet
+        return SevenNetCalculator(checkpoint_path, device="cpu")
+    elif calculator_type == "orb-v2":
+        from orb_models.forcefield import pretrained
+        from orb_models.forcefield.calculator import ORBCalculator
+        orbff = pretrained.orb_v2()
+        return ORBCalculator(orbff, device="cpu")
+    elif calculator_type == "eqV2_31M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat.pt") #adjust path to OMat24   
+    elif calculator_type == "eqV2_153M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_153M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_31M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat_mp_salex.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat_mp_salex.pt") #adjust path to OMat24
+    else:
+        raise ValueError("Unsupported calculator type")
+
+class MaterialsAnalyzer:
+    def __init__(
+        self,
+        jid=None,
+        calculator_type=None,
+        chemical_potentials_file=None,
+        film_jid=None,
+        substrate_jid=None,
+        film_index=None,
+        substrate_index=None,
+        bulk_relaxation_settings=None,
+        phonon_settings=None,
+        properties_to_calculate=None,
+        use_conventional_cell=False,
+        surface_settings=None,
+        defect_settings=None,
+        phonon3_settings=None,
+        md_settings=None
+    ):
+        self.calculator_type = calculator_type
+        self.use_conventional_cell = use_conventional_cell
+        self.chemical_potentials_file = chemical_potentials_file
+        self.bulk_relaxation_settings = bulk_relaxation_settings or {}
+        self.phonon_settings = phonon_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.properties_to_calculate = properties_to_calculate or []
+        self.surface_settings = surface_settings or {}
+        self.defect_settings = defect_settings or {}
+        self.film_index = film_index or "1_1_0"
+        self.substrate_index = substrate_index or "1_1_0"
+        self.phonon3_settings = phonon3_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.md_settings = md_settings or {'dt': 1, 'temp0': 3500, 'nsteps0': 1000, 'temp1': 300, 'nsteps1': 2000, 'taut': 20, 'min_size': 10.0}
+        if jid:
+            self.jid = jid
+            # Load atoms for the given JID
+            self.atoms = self.get_atoms(jid)
+            # Get reference data for the material
+            self.reference_data = get_entry(jid)
+            # Set up the output directory and log file
+            self.output_dir = f"{jid}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(self.output_dir, f"{jid}_job_log.txt")
+            # Initialize job_info dictionary
+            self.job_info = {
+                "jid": jid,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        elif film_jid and substrate_jid:
+            # Ensure film_jid and substrate_jid are strings, not lists
+            if isinstance(film_jid, list):
+                film_jid = film_jid[0]
+            if isinstance(substrate_jid, list):
+                substrate_jid = substrate_jid[0]
+
+            self.film_jid = film_jid
+            self.substrate_jid = substrate_jid
+
+            # Include Miller indices in directory and file names
+            self.output_dir = f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(
+                self.output_dir,
+                f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_job_log.txt",
+            )
+            self.job_info = {
+                "film_jid": film_jid,
+                "substrate_jid": substrate_jid,
+                "film_index": self.film_index,
+                "substrate_index": self.substrate_index,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        else:
+            raise ValueError("Either 'jid' or both 'film_jid' and 'substrate_jid' must be provided.")
+
+        # Set up the logger
+        self.setup_logger()
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger(self.jid or f"{self.film_jid}_{self.substrate_jid}")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        """Log information to the job log file."""
+        log_job_info(message, self.log_file)
+
+    def get_atoms(self, jid):
+        dat = get_jid_data(jid=jid, dataset="dft_3d")
+        return Atoms.from_dict(dat["atoms"])
+
+    def setup_calculator(self):
+        calc = setup_calculator(self.calculator_type)
+        self.log(f"Using calculator: {self.calculator_type}")
+        return calc
+
+    def load_chemical_potentials(self):
+        if os.path.exists(self.chemical_potentials_file):
+            with open(self.chemical_potentials_file, "r") as f:
+                return json.load(f)
+        else:
+            return {}
+
+    def save_chemical_potentials(self):
+        with open(self.chemical_potentials_file, "w") as f:
+            json.dump(self.chemical_potentials, f, indent=4)
+
+    def capture_fire_output(self, ase_atoms, fmax, steps):
+        """Capture the output of the FIRE optimizer."""
+        log_stream = io.StringIO()
+        with contextlib.redirect_stdout(log_stream):
+            dyn = FIRE(ase_atoms)
+            dyn.run(fmax=fmax, steps=steps)
+        output = log_stream.getvalue().strip()
+
+        final_energy = None
+        if output:
+            last_line = output.split("\n")[-1]
+            match = re.search(r"FIRE:\s+\d+\s+\d+:\d+:\d+\s+(-?\d+\.\d+)", last_line)
+            if match:
+                final_energy = float(match.group(1))
+
+        return final_energy, dyn.nsteps
+
+    def relax_structure(self):
+        """Perform structure relaxation and log the process."""
+        self.log(f"Starting relaxation for {self.jid}")
+
+        # Use conventional cell if specified
+
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for relaxation.")
+            self.atoms = self.atoms.get_conventional_atoms  # or appropriate method
+
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.bulk_relaxation_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.bulk_relaxation_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', False)
+        ase_atoms = self.atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+
+        # Run FIRE optimizer and capture the output using relaxation settings
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < steps
+
+        # Log the final energy and relaxation status
+        self.log(f"Final energy of FIRE optimization for structure: {final_energy}")
+        self.log(
+            f"Relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        # Update job info and save the relaxed structure
+        self.job_info["relaxed_atoms"] = relaxed_atoms.to_dict()
+        self.job_info["final_energy_structure"] = final_energy
+        self.job_info["converged"] = converged
+        self.log(f"Relaxed structure: {relaxed_atoms.to_dict()}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return relaxed_atoms if converged else None
+
+
+
+    def calculate_formation_energy(self, relaxed_atoms):
+        """
+        Calculate the formation energy per atom using the equilibrium energy and chemical potentials.
+        """
+        e0 = self.job_info["equilibrium_energy"]
+        composition = relaxed_atoms.composition.to_dict()
+        total_energy = e0
+
+        for element, amount in composition.items():
+            chemical_potential = self.get_chemical_potential(element)
+            if chemical_potential is None:
+                self.log(f"Skipping formation energy calculation due to missing chemical potential for {element}.")
+                continue  # Or handle this appropriately
+            total_energy -= chemical_potential * amount
+
+        formation_energy_per_atom = total_energy / relaxed_atoms.num_atoms
+
+    # Log and save the formation energy
+        self.job_info["formation_energy_per_atom"] = formation_energy_per_atom
+        self.log(f"Formation energy per atom: {formation_energy_per_atom}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return formation_energy_per_atom
+
+    def calculate_element_chemical_potential(self, element, element_jid):
+        """
+        Calculate the chemical potential of a pure element using its standard structure.
+        """
+        self.log(f"Calculating chemical potential for element: {element} using JID: {element_jid}")
+        try:
+            # Get standard structure for the element using the provided JID
+            element_atoms = self.get_atoms(element_jid)
+            ase_atoms = element_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+
+            # Perform energy calculation
+            energy = ase_atoms.get_potential_energy() / len(ase_atoms)
+            self.log(f"Calculated chemical potential for {element}: {energy} eV/atom")
+            return energy
+        except Exception as e:
+            self.log(f"Error calculating chemical potential for {element}: {e}")
+            return None
+
+    def get_chemical_potential(self, element):
+        """Fetch chemical potential from JSON based on the element and calculator."""
+        element_data = self.chemical_potentials.get(element, {})
+        chemical_potential = element_data.get(f"energy_{self.calculator_type}")
+
+        if chemical_potential is None:
+            self.log(
+                f"No chemical potential found for {element} with calculator {self.calculator_type}, calculating it now..."
+            )
+            # Get standard JID for the element from chemical_potentials.json
+            element_jid = element_data.get("jid")
+            if element_jid is None:
+                self.log(f"No standard JID found for element {element} in chemical_potentials.json")
+                return None  # Skip this element
+
+            # Calculate chemical potential
+            chemical_potential = self.calculate_element_chemical_potential(element, element_jid)
+            if chemical_potential is None:
+                self.log(f"Failed to calculate chemical potential for {element}")
+                return None
+            # Add it to the chemical potentials dictionary
+            if element not in self.chemical_potentials:
+                self.chemical_potentials[element] = {}
+            self.chemical_potentials[element][f"energy_{self.calculator_type}"] = chemical_potential
+            # Save the updated chemical potentials to file
+            self.save_chemical_potentials()
+
+        return chemical_potential
+    def calculate_forces(self, atoms):
+        """
+        Calculate the forces on the given atoms without performing relaxation.
+        """
+        self.log(f"Calculating forces for {self.jid}")
+
+    # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+    # Calculate forces
+        forces = ase_atoms.get_forces()  # This returns an array of forces
+
+    # Log and save the forces
+        self.job_info['forces'] = forces.tolist()  # Convert to list for JSON serialization
+        self.log(f"Forces calculated: {forces}")
+
+    # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return forces
+
+    def calculate_ev_curve(self, relaxed_atoms):
+        """Calculate the energy-volume (E-V) curve and log results."""
+        self.log(f"Calculating EV curve for {self.jid}")
+
+        dx = np.arange(-0.06, 0.06, 0.01)  # Strain values
+        y = []  # Energies
+        vol = []  # Volumes
+        strained_structures = []  # To store strained structures
+
+        for i in dx:
+            # Apply strain and calculate energy at each strain value
+            strained_atoms = relaxed_atoms.strain_atoms(i)
+            strained_structures.append(strained_atoms)
+            ase_atoms = strained_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+            energy = ase_atoms.get_potential_energy()
+
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+        # Convert data to numpy arrays for processing
+        y = np.array(y)
+        vol = np.array(vol)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        try:
+            eos = EquationOfState(vol, y, eos="murnaghan")
+            v0, e0, B = eos.fit()
+
+            # Bulk modulus in GPa (conversion factor from ASE units)
+            kv = B / kJ * 1.0e24  # Convert to GPa
+
+            # Log important results
+            self.log(f"Bulk modulus: {kv} GPa")
+            self.log(f"Equilibrium energy: {e0} eV")
+            self.log(f"Equilibrium volume: {v0} ų")
+
+            # Save E-V curve plot
+            fig = plt.figure()
+            eos.plot()
+            ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+            fig.savefig(ev_plot_filename)
+            plt.close(fig)
+            self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+            # Save E-V curve data to a text file
+            ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+            with open(ev_data_filename, "w") as f:
+                f.write("Volume (ų)\tEnergy (eV)\n")
+                for v, e in zip(vol, y):
+                    f.write(f"{v}\t{e}\n")
+            self.log(f"E-V curve data saved to {ev_data_filename}")
+
+            # Update job info with the results
+            self.job_info["bulk_modulus"] = kv
+            self.job_info["equilibrium_energy"] = e0
+            self.job_info["equilibrium_volume"] = v0
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        except RuntimeError as e:
+            self.log(f"Error fitting EOS for {self.jid}: {e}")
+            self.log("Skipping bulk modulus calculation due to fitting error.")
+            kv = None  # Set bulk modulus to None or handle this as you wish
+            e0, v0 = None, None  # Set equilibrium energy and volume to None
+
+        # Return additional values for thermal expansion analysis
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+
+    def calculate_elastic_tensor(self, relaxed_atoms):
+        import elastic
+        from elastic import get_elementary_deformations, get_elastic_tensor
+        """
+        Calculate the elastic tensor for the relaxed structure using the provided calculator.
+        """
+        self.log(f"Starting elastic tensor calculation for {self.jid}")
+        start_time = time.time()
+
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Get elementary deformations for elastic tensor calculation
+        systems = elastic.get_elementary_deformations(ase_atoms)
+
+        # Calculate the elastic tensor and convert to GPa
+        cij_order = elastic.elastic.get_cij_order(ase_atoms)
+        Cij, Bij = elastic.get_elastic_tensor(ase_atoms, systems)
+        elastic_tensor = {
+            i: j / ase.units.GPa for i, j in zip(cij_order, Cij)
+        }  # Convert to GPa
+
+        # Save and log the results
+        self.job_info["elastic_tensor"] = elastic_tensor
+        self.log(
+            f"Elastic tensor for {self.jid} with {self.calculator_type}: {elastic_tensor}"
+        )
+
+        # Timing the calculation
+        end_time = time.time()
+        self.log(f"Elastic Calculation time: {end_time - start_time} seconds")
+
+        # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return elastic_tensor
+
+    def run_phonon_analysis(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Perform Phonon calculation, generate force constants, and plot band structure & DOS."""
+        self.log(f"Starting phonon analysis for {self.jid}")
+        phonopy_bands_figname = f"ph_{self.jid}_{self.calculator_type}.png"
+
+        # Phonon generation parameters
+        dim = self.phonon_settings.get('dim', [2, 2, 2])
+        # Define the conversion factor from THz to cm^-1
+        THz_to_cm = 33.35641  # 1 THz = 33.35641 cm^-1
+
+        force_constants_filename = "FORCE_CONSTANTS"
+        eigenvalues_filename = "phonon_eigenvalues.txt"
+        thermal_props_filename = "thermal_properties.txt"
+        write_fc = True
+        min_freq_tol_cm = -5.0  # in cm^-1
+        distance = self.phonon_settings.get('distance', 0.2)
+
+        # Generate k-point path
+        kpoints = Kpoints().kpath(relaxed_atoms, line_density=5)
+
+        # Convert atoms to Phonopy-compatible object
+        self.log("Converting atoms to Phonopy-compatible format...")
+        bulk = relaxed_atoms.phonopy_converter()
+        phonon = Phonopy(
+            bulk,
+            [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]],
+            # Do not set factor here to keep frequencies in THz during calculations
+        )
+
+        # Generate displacements
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+        self.log(f"Generated {len(supercells)} supercells for displacements.")
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for idx, scell in enumerate(supercells):
+            self.log(f"Calculating forces for supercell {idx+1}...")
+            ase_atoms = AseAtoms(
+                symbols=scell.symbols,
+                positions=scell.positions,
+                cell=scell.cell,
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+
+            # Correct for drift force
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+
+            set_of_forces.append(forces)
+
+        # Generate force constants
+        self.log("Producing force constants...")
+        phonon.produce_force_constants(forces=set_of_forces)
+
+        # Write force constants to file if required
+        if write_fc:
+            force_constants_filepath = os.path.join(
+                self.output_dir, force_constants_filename
+            )
+            self.log(f"Writing force constants to {force_constants_filepath}...")
+            write_FORCE_CONSTANTS(
+                phonon.force_constants, filename=force_constants_filepath
+            )
+            self.log(f"Force constants saved to {force_constants_filepath}")
+
+        # Prepare bands for band structure calculation
+        bands = [kpoints.kpts]  # Assuming kpoints.kpts is a list of q-points
+
+        # Prepare labels and path_connections
+        labels = []
+        path_connections = []
+        for i, label in enumerate(kpoints.labels):
+            if label:
+                labels.append(label)
+            else:
+                labels.append("")  # Empty string for points without labels
+
+        # Since we have a single path, set path_connections accordingly
+        path_connections = [True] * (len(bands) - 1)
+        path_connections.append(False)
+
+        # Run band structure calculation with labels
+        self.log("Running band structure calculation...")
+        phonon.run_band_structure(
+            bands,
+            with_eigenvectors=False,
+            labels=labels,
+            path_connections=path_connections,
+        )
+
+        # Write band.yaml file (frequencies will be in THz)
+        band_yaml_filepath = os.path.join(self.output_dir, "band.yaml")
+        self.log(f"Writing band structure data to {band_yaml_filepath}...")
+        phonon.band_structure.write_yaml(filename=band_yaml_filepath)
+        self.log(f"band.yaml saved to {band_yaml_filepath}")
+
+        # --- Begin post-processing to convert frequencies to cm^-1 while preserving formatting ---
+        from ruamel.yaml import YAML
+
+        self.log(f"Converting frequencies in {band_yaml_filepath} to cm^-1 while preserving formatting...")
+        yaml = YAML()
+        yaml.preserve_quotes = True
+
+        with open(band_yaml_filepath, 'r') as f:
+            band_data = yaml.load(f)
+
+        for phonon_point in band_data['phonon']:
+            for band in phonon_point['band']:
+                freq = band['frequency']
+                if freq is not None:
+                    band['frequency'] = freq * THz_to_cm
+
+        with open(band_yaml_filepath, 'w') as f:
+            yaml.dump(band_data, f)
+
+        self.log(f"Frequencies in {band_yaml_filepath} converted to cm^-1 with formatting preserved")
+        # --- End post-processing ---
+
+        # Phonon band structure and eigenvalues
+        lbls = kpoints.labels
+        lbls_ticks = []
+        freqs = []
+        lbls_x = []
+        count = 0
+        eigenvalues = []
+
+        for ii, k in enumerate(kpoints.kpts):
+            k_str = ",".join(map(str, k))
+            if ii == 0 or k_str != ",".join(map(str, kpoints.kpts[ii - 1])):
+                freqs_at_k = phonon.get_frequencies(k)  # Frequencies in THz
+                freqs_at_k_cm = freqs_at_k * THz_to_cm  # Convert to cm^-1
+                freqs.append(freqs_at_k_cm)
+                eigenvalues.append((k, freqs_at_k_cm))  # Store frequencies in cm^-1
+                lbl = "$" + str(lbls[ii]) + "$" if lbls[ii] else ""
+                if lbl:
+                    lbls_ticks.append(lbl)
+                    lbls_x.append(count)
+                count += 1
+
+        # Write eigenvalues to file with frequencies in cm^-1
+        eigenvalues_filepath = os.path.join(self.output_dir, eigenvalues_filename)
+        self.log(f"Writing phonon eigenvalues to {eigenvalues_filepath}...")
+        with open(eigenvalues_filepath, "w") as eig_file:
+            eig_file.write("k-points\tFrequencies (cm^-1)\n")
+            for k, freqs_at_k_cm in eigenvalues:
+                k_str = ",".join(map(str, k))
+                freqs_str = "\t".join(map(str, freqs_at_k_cm))
+                eig_file.write(f"{k_str}\t{freqs_str}\n")
+        self.log(f"Phonon eigenvalues saved to {eigenvalues_filepath}")
+
+        # Convert frequencies to numpy array in cm^-1
+        freqs = np.array(freqs)
+
+        # Plot phonon band structure and DOS
+        the_grid = plt.GridSpec(1, 2, width_ratios=[3, 1], wspace=0.0)
+        plt.rcParams.update({"font.size": 18})
+        plt.figure(figsize=(10, 5))
+
+        # Plot phonon bands
+        plt.subplot(the_grid[0])
+        for i in range(freqs.shape[1]):
+            plt.plot(freqs[:, i], lw=2, c="b")
+        for i in lbls_x:
+            plt.axvline(x=i, c="black")
+        plt.xticks(lbls_x, lbls_ticks)
+        plt.ylabel("Frequency (cm$^{-1}$)")
+        plt.xlim([0, max(lbls_x)])
+
+        # Run mesh and DOS calculations
+        phonon.run_mesh([40, 40, 40], is_gamma_center=True, is_mesh_symmetry=False)
+        phonon.run_total_dos()
+        tdos = phonon.total_dos
+        freqs_dos = np.array(tdos.frequency_points) * THz_to_cm  # Convert to cm^-1
+        dos_values = tdos.dos
+        min_freq = min_freq_tol_cm  # in cm^-1
+        max_freq = max(freqs_dos)
+
+        plt.ylim([min_freq, max_freq])
+
+        # Plot DOS
+        plt.subplot(the_grid[1])
+        plt.fill_between(
+            dos_values, freqs_dos, color=(0.2, 0.4, 0.6, 0.6), edgecolor="k", lw=1, y2=0
+        )
+        plt.xlabel("DOS")
+        plt.yticks([])
+        plt.xticks([])
+        plt.ylim([min_freq, max_freq])
+        plt.xlim([0, max(dos_values)])
+
+        # Save the plot
+        os.makedirs(self.output_dir, exist_ok=True)
+        plot_filepath = os.path.join(self.output_dir, phonopy_bands_figname)
+        plt.tight_layout()
+        plt.savefig(plot_filepath)
+        self.log(
+            f"Phonon band structure and DOS combined plot saved to {plot_filepath}"
+        )
+        plt.close()
+
+        self.log("Calculating thermal properties...")
+        phonon.run_mesh(mesh=[20, 20, 20])
+        phonon.run_thermal_properties(t_step=10, t_max=1000, t_min=0)
+        tprop_dict = phonon.get_thermal_properties_dict()
+
+        # Plot thermal properties
+        plt.figure()
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["free_energy"],
+            label="Free energy (kJ/mol)",
+            color="red",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["entropy"],
+            label="Entropy (J/K*mol)",
+            color="blue",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["heat_capacity"],
+            label="Heat capacity (J/K*mol)",
+            color="green",
+        )
+        plt.legend()
+        plt.xlabel("Temperature (K)")
+        plt.ylabel("Thermal Properties")
+        plt.title("Thermal Properties")
+
+        thermal_props_plot_filepath = os.path.join(
+            self.output_dir, f"Thermal_Properties_{self.jid}.png"
+        )
+        plt.savefig(thermal_props_plot_filepath)
+        self.log(f"Thermal properties plot saved to {thermal_props_plot_filepath}")
+        plt.close()
+
+        # Save thermal properties to file
+        thermal_props_filepath = os.path.join(self.output_dir, thermal_props_filename)
+        with open(thermal_props_filepath, "w") as f:
+            f.write(
+                "Temperature (K)\tFree Energy (kJ/mol)\tEntropy (J/K*mol)\tHeat Capacity (J/K*mol)\n"
+            )
+            for i in range(len(tprop_dict["temperatures"])):
+                f.write(
+                    f"{tprop_dict['temperatures'][i]}\t{tprop_dict['free_energy'][i]}\t"
+                    f"{tprop_dict['entropy'][i]}\t{tprop_dict['heat_capacity'][i]}\n"
+                )
+        self.log(f"Thermal properties written to {thermal_props_filepath}")
+
+        # Calculate zero-point energy (ZPE)
+        zpe = tprop_dict["free_energy"][0] * 0.0103643  # Converting from kJ/mol to eV
+        self.log(f"Zero-point energy: {zpe} eV")
+
+        # Save to job info
+        self.job_info["phonopy_bands"] = phonopy_bands_figname
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return phonon, zpe
+
+    def analyze_defects(self):
+        """Analyze defects by generating, relaxing, and calculating vacancy formation energy."""
+        self.log("Starting defect analysis...")
+        generate_settings = self.defect_settings.get('generate_settings', {})
+        on_conventional_cell = generate_settings.get('on_conventional_cell', True)
+        enforce_c_size = generate_settings.get('enforce_c_size', 8)
+        extend = generate_settings.get('extend', 1)
+    # Generate defect structures from the original atoms
+        defect_structures = Vacancy(self.atoms).generate_defects(on_conventional_cell=on_conventional_cell, enforce_c_size=enforce_c_size, extend=extend)
+
+        for defect in defect_structures:
+        # Extract the defect structure and related metadata
+            defect_structure = Atoms.from_dict(defect.to_dict()["defect_structure"])
+        
+        # Construct a consistent defect name without Wyckoff notation
+            element = defect.to_dict()['symbol']
+            defect_name = f"{self.jid}_{element}"  # Consistent format
+            self.log(f"Analyzing defect: {defect_name}")
+
+        # Relax the defect structure
+            relaxed_defect_atoms = self.relax_defect_structure(defect_structure, name=defect_name)
+
+            if relaxed_defect_atoms is None:
+                self.log(f"Skipping {defect_name} due to failed relaxation.")
+                continue
+
+        # Retrieve energies for calculating the vacancy formation energy
+            vacancy_energy = self.job_info.get(f"final_energy_defect for {defect_name}")
+            bulk_energy = (
+                self.job_info.get("equilibrium_energy")
+                / self.atoms.num_atoms
+                * (defect_structure.num_atoms + 1)
+            )
+
+            if vacancy_energy is None or bulk_energy is None:
+                self.log(f"Skipping {defect_name} due to missing energy values.")
+                continue
+
+        # Get chemical potential and calculate vacancy formation energy
+            chemical_potential = self.get_chemical_potential(element)
+
+            if chemical_potential is None:
+                self.log(f"Skipping {defect_name} due to missing chemical potential for {element}.")
+                continue
+
+            vacancy_formation_energy = vacancy_energy - bulk_energy + chemical_potential
+
+        # Log and store the vacancy formation energy consistently
+            self.job_info[f"vacancy_formation_energy for {defect_name}"] = vacancy_formation_energy
+            self.log(f"Vacancy formation energy for {defect_name}: {vacancy_formation_energy} eV")
+
+    # Save the job info to a JSON file
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+        self.log("Defect analysis completed.")
+
+    def relax_defect_structure(self, atoms, name):
+        """Relax the defect structure and log the process."""
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.defect_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.defect_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log the final energy and relaxation status
+        self.log(
+            f"Final energy of FIRE optimization for defect structure: {final_energy}"
+        )
+        self.log(
+            f"Defect relaxation {'converged' if converged else 'did not converge'} within 200 steps."
+        )
+
+        # Update job info with the final energy and convergence status
+        self.job_info[f"final_energy_defect for {name}"] = final_energy
+        self.job_info[f"converged for {name}"] = converged
+
+        if converged:
+            poscar_filename = os.path.join(self.output_dir, f"POSCAR_{name}_relaxed.vasp")
+            poscar_defect = Poscar(relaxed_atoms)
+            poscar_defect.write_file(poscar_filename)
+            self.log(f"Relaxed defect structure saved to {poscar_filename}")
+
+        return relaxed_atoms if converged else None
+
+    def analyze_surfaces(self):
+        """
+        Perform surface analysis by generating surface structures, relaxing them, and calculating surface energies.
+        """
+        self.log(f"Analyzing surfaces for {self.jid}")
+
+        indices_list = self.surface_settings.get('indices_list', [
+            [1, 0, 0],
+            [1, 1, 1],
+            [1, 1, 0],
+            [0, 1, 1],
+            [0, 0, 1],
+            [0, 1, 0],
+        ])
+        layers = self.surface_settings.get('layers', 4)
+        vacuum = self.surface_settings.get('vacuum', 18)
+
+        for indices in indices_list:
+            # Generate surface and check for polarity
+            surface = (
+                Surface(atoms=self.atoms, indices=indices, layers=layers, vacuum=vacuum)
+                .make_surface()
+                .center_around_origin()
+            )
+            if surface.check_polar:
+                self.log(
+                    f"Skipping polar surface for {self.jid} with indices {indices}"
+                )
+                continue
+
+            # Write initial POSCAR for surface
+            poscar_surface = Poscar(atoms=surface)
+            poscar_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}.vasp",
+                )
+            )
+
+            # Relax the surface structure
+            relaxed_surface_atoms, final_energy = self.relax_surface_structure(
+                surface, indices
+            )
+
+            # If relaxation failed, skip further calculations
+            if relaxed_surface_atoms is None:
+                self.log(f"Skipping surface {indices} due to failed relaxation.")
+                continue
+
+            # Write relaxed POSCAR for surface
+            pos_relaxed_surface = Poscar(relaxed_surface_atoms)
+            pos_relaxed_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}_relaxed.vasp",
+                )
+            )
+
+            # Calculate and log surface energy
+            bulk_energy = self.job_info.get("equilibrium_energy")
+            if final_energy is None or bulk_energy is None:
+                self.log(
+                    f"Skipping surface energy calculation for {self.jid} with indices {indices} due to missing energy values."
+                )
+                continue
+
+            surface_energy = self.calculate_surface_energy(
+                final_energy, bulk_energy, relaxed_surface_atoms, surface
+            )
+
+            # Store the surface energy with the new naming convention
+            surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+            self.job_info[surface_name] = surface_energy
+            self.log(
+                f"Surface energy for {self.jid} with indices {indices}: {surface_energy} J/m^2"
+            )
+
+        # Save updated job info
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log("Surface analysis completed.")
+
+    def relax_surface_structure(self, atoms, indices):
+        """
+        Relax the surface structure and log the process.
+        """
+        filter_type = self.surface_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.surface_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        self.log(f"Starting surface relaxation for {self.jid} with indices {indices}")
+        start_time = time.time()
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+        # Implement other filters if needed
+            pass
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log relaxation results
+        self.log(
+            f"Final energy of FIRE optimization for surface structure: {final_energy}"
+        )
+        self.log(
+            f"Surface relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        end_time = time.time()
+        self.log(
+            f"Surface Relaxation Calculation time: {end_time - start_time} seconds"
+        )
+
+        # Update job info and return relaxed atoms if converged, otherwise return None
+        self.job_info[f"final_energy_surface_{indices}"] = final_energy
+        self.job_info[f"converged_surface_{indices}"] = converged
+
+        # Return both relaxed atoms and the final energy as a tuple
+        return (relaxed_atoms if converged else None), final_energy
+
+    def calculate_surface_energy(
+        self, final_energy, bulk_energy, relaxed_atoms, surface
+    ):
+        """
+        Calculate the surface energy based on the final energy of the relaxed surface and bulk energy.
+        """
+        # Calculate the number of bulk units in the surface supercell
+        num_units = surface.num_atoms / self.atoms.num_atoms
+
+        # Calculate the surface area using the lattice vectors
+        m = relaxed_atoms.lattice.matrix
+        surface_area = np.linalg.norm(np.cross(m[0], m[1]))
+
+        # Calculate surface energy in J/m^2
+        surface_energy = (
+            (final_energy - bulk_energy * num_units) * 16.02176565 / (2 * surface_area)
+        )
+
+        return surface_energy
+
+    def run_phonon3_analysis(self, relaxed_atoms):
+        from phono3py import Phono3py
+        """Run Phono3py analysis, process results, and generate thermal conductivity data."""
+        self.log(f"Starting Phono3py analysis for {self.jid}")
+
+        # Set parameters for the Phono3py calculation
+        dim = self.phonon3_settings.get('dim', [2, 2, 2])
+        distance = self.phonon3_settings.get('distance', 0.2)
+
+        #force_multiplier = 16
+
+        # Convert atoms to Phonopy-compatible object and set up Phono3py
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        bulk = relaxed_atoms.phonopy_converter()
+
+        phonon = Phono3py(bulk, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]])
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for scell in supercells:
+            ase_atoms = AseAtoms(
+                symbols=scell.get_chemical_symbols(),
+                scaled_positions=scell.get_scaled_positions(),
+                cell=scell.get_cell(),
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+            set_of_forces.append(forces)
+
+        # Set the forces and produce third-order force constants
+        forces = np.array(set_of_forces).reshape(-1, len(phonon.supercell), 3)
+        phonon.forces = forces
+        phonon.produce_fc3()
+
+        # Run thermal conductivity calculation
+        phonon.mesh_numbers = 30
+        phonon.init_phph_interaction()
+        phonon.run_thermal_conductivity(
+            temperatures=range(0, 1001, 10), write_kappa=True
+        )
+
+        # Collect thermal conductivity data
+        kappa = phonon.thermal_conductivity.kappa
+        self.log(f"Thermal conductivity: {kappa}")
+
+        # Move generated HDF5 files to the output directory
+        hdf5_file_pattern = "kappa-*.hdf5"
+        for hdf5_file in glob.glob(hdf5_file_pattern):
+            shutil.move(hdf5_file, os.path.join(self.output_dir, hdf5_file))
+
+        # Process Phono3py results and save plots
+        self.process_phonon3_results()
+
+        # Save updated job info to JSON
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(f"Phono3py analysis completed for {self.jid}")
+
+    def process_phonon3_results(self):
+        """Process Phono3py results and generate plots of thermal conductivity."""
+        file_pattern = os.path.join(self.output_dir, "kappa-*.hdf5")
+        file_list = glob.glob(file_pattern)
+
+        temperatures = np.arange(10, 101, 10)
+        kappa_xx_values = []
+
+        if file_list:
+            hdf5_filename = file_list[0]
+            self.log(f"Processing file: {hdf5_filename}")
+
+            for temperature_index in temperatures:
+                converted_kappa = self.convert_kappa_units(
+                    hdf5_filename, temperature_index
+                )
+                kappa_xx = converted_kappa[0]
+                kappa_xx_values.append(kappa_xx)
+                self.log(
+                    f"Temperature index {temperature_index}, converted kappa: {kappa_xx}"
+                )
+
+            # Save results to job_info
+            self.job_info["temperatures"] = temperatures.tolist()
+            self.job_info["kappa_xx_values"] = kappa_xx_values
+
+            # Plot temperature vs. converted kappa (xx element)
+            plt.figure(figsize=(8, 6))
+            plt.plot(
+                temperatures * 10, kappa_xx_values, marker="o", linestyle="-", color="b"
+            )
+            plt.xlabel("Temperature (K)")
+            plt.ylabel("Converted Kappa (xx element)")
+            plt.title("Temperature vs. Converted Kappa (xx element)")
+            plt.grid(True)
+            plt.savefig(
+                os.path.join(self.output_dir, "Temperature_vs_Converted_Kappa.png")
+            )
+            plt.close()
+        else:
+            self.log("No files matching the pattern were found.")
+
+    def convert_kappa_units(self, hdf5_filename, temperature_index):
+        """Convert thermal conductivity kappa from HDF5 file units."""
+        with h5py.File(hdf5_filename, "r") as f:
+            kappa_unit_conversion = f["kappa_unit_conversion"][()]
+            heat_capacity = f["heat_capacity"][:]
+            gv_by_gv = f["gv_by_gv"][:]
+            gamma = f["gamma"][:]
+
+            converted_kappa = (
+                kappa_unit_conversion
+                * heat_capacity[temperature_index, 2, 0]
+                * gv_by_gv[2, 0]
+                / (2 * gamma[temperature_index, 2, 0])
+            )
+
+            return converted_kappa
+
+    def calculate_thermal_expansion(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Calculate the thermal expansion coefficient using QHA."""
+
+        def log(message):
+            with open(self.log_file, "a") as f:
+                f.write(message + "\n")
+            print(message)
+
+        log("Starting thermal expansion analysis...")
+
+        # Step 1: Calculate finer E-V curve
+        vol, y, strained_structures, eos, kv, e0, v0 = self.fine_ev_curve(
+            atoms=relaxed_atoms, dx=np.linspace(-0.05, 0.05, 50)  # Denser grid
+        )
+
+        # Log Bulk modulus, equilibrium energy, and volume
+        log(
+            f"Bulk modulus: {kv} GPa, Equilibrium energy: {y[0]} eV, Volume: {vol[0]} ų"
+        )
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = y[0]
+        self.job_info["equilibrium_volume"] = vol[0]
+
+        # Step 2: Generate phonons for strained structures
+        free_energies, heat_capacities, entropies, temperatures = (
+            self.generate_phonons_for_volumes(
+                strained_structures,
+                calculator=self.calculator,
+                dim=[2, 2, 2],
+                distance=0.2,
+                mesh=[20, 20, 20],
+            )
+        )
+
+        # Step 3: Perform QHA-based thermal expansion analysis
+        alpha = self.perform_qha(
+            volumes=vol,
+            energies=y,
+            free_energies=free_energies,
+            heat_capacities=heat_capacities,
+            entropies=entropies,
+            temperatures=temperatures,
+            output_dir=self.output_dir,
+        )
+
+        self.log(f"Thermal expansion coefficient calculated: {alpha}")
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(
+            f"Thermal expansion analysis information saved to file: {self.jid}_{self.calculator_type}_job_info.json"
+        )
+
+    # Helper Functions Inside the Class
+    def fine_ev_curve(self, atoms, dx=np.linspace(-0.05, 0.05, 50)):
+        """
+        Generate a finer energy-volume curve for strained structures.
+        """
+        y = []
+        vol = []
+        strained_structures = []
+
+        for i in dx:
+            # Apply strain and get strained atoms
+            strained_atoms = atoms.strain_atoms(i)
+            ase_atoms = strained_atoms.ase_converter()  # Convert to ASE Atoms
+            ase_atoms.calc = self.calculator  # Assign the calculator
+
+            # Get potential energy and volume
+            energy = ase_atoms.get_potential_energy()
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+            strained_structures.append(strained_atoms)  # Save the strained structure
+
+        vol = np.array(vol)
+        y = np.array(y)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        eos = EquationOfState(vol, y, eos="murnaghan")
+        v0, e0, B = eos.fit()
+        kv = B / kJ * 1.0e24  # Convert to GPa
+
+        # Log important results
+        self.log(f"Bulk modulus: {kv} GPa")
+        self.log(f"Equilibrium energy: {e0} eV")
+        self.log(f"Equilibrium volume: {v0} ų")
+
+        # Save E-V curve plot
+        fig = plt.figure()
+        eos.plot()
+        ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+        fig.savefig(ev_plot_filename)
+        plt.close(fig)
+        self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+        # Save E-V curve data to a text file
+        ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+        with open(ev_data_filename, "w") as f:
+            f.write("Volume (ų)\tEnergy (eV)\n")
+            for v, e in zip(vol, y):
+                f.write(f"{v}\t{e}\n")
+        self.log(f"E-V curve data saved to {ev_data_filename}")
+
+        # Update job info with the results
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = e0
+        self.job_info["equilibrium_volume"] = v0
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+    def generate_phonons_for_volumes(
+        self, structures, calculator, dim=[2, 2, 2], distance=0.2, mesh=[20, 20, 20]
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        all_free_energies = []
+        all_heat_capacities = []
+        all_entropies = []
+        temperatures = np.arange(0, 300, 6)  # Define temperature range
+
+        for structure in structures:
+            # Convert structure to PhonopyAtoms
+            phonopy_atoms = PhonopyAtoms(
+                symbols=[str(e) for e in structure.elements],
+                positions=structure.cart_coords,
+                cell=structure.lattice.matrix,
+            )
+
+            # Initialize Phonopy object
+            phonon = Phonopy(
+                phonopy_atoms, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]]
+            )
+            phonon.generate_displacements(distance=distance)
+
+            # Calculate forces for displaced structures
+            supercells = phonon.get_supercells_with_displacements()
+            forces = []
+            for scell in supercells:
+                ase_atoms = AseAtoms(
+                    symbols=scell.symbols,
+                    positions=scell.positions,
+                    cell=scell.cell,
+                    pbc=True,
+                )
+                ase_atoms.calc = calculator
+                forces.append(ase_atoms.get_forces())
+
+            phonon.produce_force_constants(forces=forces)
+
+            # Post-processing to get thermal properties
+            phonon.run_mesh(mesh=mesh)
+            phonon.run_thermal_properties(t_min=0, t_step=6, t_max=294)
+            tprop_dict = phonon.get_thermal_properties_dict()
+
+            free_energies = tprop_dict["free_energy"]
+            heat_capacities = tprop_dict["heat_capacity"]
+            entropies = tprop_dict["entropy"]
+
+            all_entropies.append(entropies)
+            all_free_energies.append(free_energies)
+            all_heat_capacities.append(heat_capacities)
+
+        return (
+            np.array(all_free_energies),
+            np.array(all_heat_capacities),
+            np.array(all_entropies),
+            temperatures,
+        )
+
+    def perform_qha(
+        self,
+        volumes,
+        energies,
+        free_energies,
+        heat_capacities,
+        entropies,
+        temperatures,
+        output_dir,
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        # Debugging: print array sizes
+        print(f"Number of temperatures: {len(temperatures)}")
+        print(f"Number of free energy data points: {free_energies.shape}")
+        print(f"Number of volume data points: {len(volumes)}")
+
+        # Ensure that volumes, free_energies, and temperatures are consistent
+        if len(volumes) != len(temperatures):
+            raise ValueError(
+                "The number of volumes must match the number of temperatures"
+            )
+
+        # Initialize the QHA object
+        try:
+            qha = PhonopyQHA(
+                volumes=volumes,
+                electronic_energies=energies,
+                free_energy=free_energies,
+                cv=heat_capacities,
+                entropy=entropies,
+                temperatures=temperatures,
+                eos="murnaghan",  # or another EOS if needed
+                verbose=True,
+            )
+        except IndexError as e:
+            print(f"Error in QHA initialization: {e}")
+            raise
+
+        # Calculate thermal expansion and save plots
+        thermal_expansion_plot = os.path.join(output_dir, "thermal_expansion.png")
+        volume_temperature_plot = os.path.join(output_dir, "volume_temperature.png")
+        helmholtz_volume_plot = os.path.join(output_dir, "helmholtz_volume.png")
+
+        qha.get_thermal_expansion()
+
+        # Save thermal expansion plot
+        qha.plot_thermal_expansion()
+        plt.savefig(thermal_expansion_plot)
+
+        # Save volume-temperature plot
+        qha.plot_volume_temperature()
+        plt.savefig(volume_temperature_plot)
+
+        # Save Helmholtz free energy vs. volume plot
+        qha.plot_helmholtz_volume()
+        plt.savefig(helmholtz_volume_plot)
+
+        # Optionally save thermal expansion coefficient to a file
+        thermal_expansion_file = os.path.join(output_dir, "thermal_expansion.txt")
+        alpha = qha.write_thermal_expansion(filename=thermal_expansion_file)
+
+        return alpha
+
+    def general_melter(self, relaxed_atoms):
+        """Perform MD simulation to melt the structure, then quench it back to room temperature."""
+        self.log(f"Starting MD melting and quenching simulation for {self.jid}")
+
+        calculator = self.setup_calculator()
+        ase_atoms = relaxed_atoms.ase_converter()
+        dim = self.ensure_cell_size(ase_atoms, min_size=self.md_settings.get('min_size', 10.0))
+        supercell = relaxed_atoms.make_supercell_matrix(dim)
+        ase_atoms = supercell.ase_converter()
+        ase_atoms.calc = calculator
+
+        dt = self.md_settings.get('dt', 1) * ase.units.fs
+        temp0 = self.md_settings.get('temp0', 3500)
+        nsteps0 = self.md_settings.get('nsteps0', 1000)
+        temp1 = self.md_settings.get('temp1', 300)
+        nsteps1 = self.md_settings.get('nsteps1', 2000)
+        taut = self.md_settings.get('taut', 20) * ase.units.fs
+        trj = os.path.join(self.output_dir, f"{self.jid}_melt.traj")
+
+        # Initialize velocities and run the first part of the MD simulation
+        from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+        from ase.md.nvtberendsen import NVTBerendsen
+
+        MaxwellBoltzmannDistribution(ase_atoms, temp0 * ase.units.kB)
+        dyn = NVTBerendsen(ase_atoms, dt, temp0, taut=taut, trajectory=trj)
+
+        def myprint():
+            message = f"time={dyn.get_time() / ase.units.fs: 5.0f} fs T={ase_atoms.get_temperature(): 3.0f} K"
+            self.log(message)
+
+        dyn.attach(myprint, interval=20)
+        dyn.run(nsteps0)
+
+        # Cool down to room temperature
+        dyn.set_temperature(temp1)
+        dyn.run(nsteps1)
+
+        # Convert back to JARVIS atoms and save the final structure
+        final_atoms = ase_to_atoms(ase_atoms)
+        poscar_filename = os.path.join(
+            self.output_dir, f"POSCAR_{self.jid}_quenched_{self.calculator_type}.vasp"
+        )
+        from ase.io import write
+
+        write(poscar_filename, final_atoms.ase_converter(), format="vasp")
+        self.log(f"MD simulation completed. Final structure saved to {poscar_filename}")
+        self.job_info["quenched_atoms"] = final_atoms.to_dict()
+
+        return final_atoms
+
+    def calculate_rdf(self, quenched_atoms):
+        """Calculate Radial Distribution Function (RDF) for quenched structure and save plot."""
+        self.log(f"Starting RDF calculation for {self.jid}")
+        ase_atoms = quenched_atoms.ase_converter()
+        rmax = 3.5
+        nbins = 200
+
+        def perform_rdf_calculation(rmax):
+            from ase.ga.utilities import get_rdf
+
+            rdfs, distances = get_rdf(ase_atoms, rmax, nbins)
+            plt.figure()
+            plt.plot(distances, rdfs)
+            plt.xlabel("Distance (Å)")
+            plt.ylabel("RDF")
+            plt.title(
+                f"Radial Distribution Function for {self.jid} ({self.calculator_type})"
+            )
+            rdf_plot_filename = os.path.join(
+                self.output_dir, f"RDF_{self.jid}_{self.calculator_type}.png"
+            )
+            plt.savefig(rdf_plot_filename)
+            plt.close()
+            self.job_info["rdf_plot"] = rdf_plot_filename
+            self.log(f"RDF plot saved to {rdf_plot_filename}")
+            return rdf_plot_filename
+
+        try:
+            perform_rdf_calculation(rmax)
+        except ValueError as e:
+            if "The cell is not large enough" in str(e):
+                recommended_rmax = float(str(e).split("<")[1].split("=")[1])
+                self.log(f"Error: {e}. Adjusting rmax to {recommended_rmax}.")
+                perform_rdf_calculation(recommended_rmax)
+            else:
+                self.log(f"Error: {e}")
+                raise
+
+    def ensure_cell_size(self, ase_atoms, min_size):
+        """Ensure that all cell dimensions are at least min_size."""
+        cell_lengths = ase_atoms.get_cell().lengths()
+        scale_factors = np.ceil(min_size / cell_lengths).astype(int)
+        supercell_dims = [max(1, scale) for scale in scale_factors]
+        return supercell_dims
+
+    def analyze_interfaces(self):
+        """Perform interface analysis using intermat package."""
+        if not self.film_jid or not self.substrate_jid:
+            self.log("Film JID or substrate JID not provided, skipping interface analysis.")
+            return
+
+        self.log(f"Starting interface analysis between {self.film_jid} and {self.substrate_jid}")
+
+        # Ensure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # Prepare config
+        config = {
+            "film_jid": self.film_jid,
+            "substrate_jid": self.substrate_jid,
+            "film_index": self.film_index,
+            "substrate_index": self.substrate_index,
+            "disp_intvl": 0.05,
+            "calculator_method": self.calculator_type.lower(),
+        }
+
+        config_filename = os.path.join(
+            self.output_dir,
+            f"config_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.json"
+        )
+
+        # Save config file
+        save_dict_to_json(config, config_filename)
+        self.log(f"Config file created: {config_filename}")
+
+        # Run intermat script using subprocess in self.output_dir
+        command = f"run_intermat.py --config_file {os.path.basename(config_filename)}"
+        self.log(f"Running command: {command}")
+
+        try:
+            result = subprocess.run(
+                command,
+                shell=True,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=self.output_dir  # Set the working directory for the subprocess
+            )
+            self.log(f"Command output: {result.stdout}")
+        except subprocess.CalledProcessError as e:
+            self.log(f"Command failed with error: {e.stderr}")
+            return
+
+        # After execution, check for outputs in self.output_dir
+        main_results_filename = os.path.join(self.output_dir, "intermat_results.json")
+        if not os.path.exists(main_results_filename):
+            self.log(f"Results file not found: {main_results_filename}")
+            return
+
+        res = load_dict_from_json(main_results_filename)
+        w_adhesion = res.get("wads", [])
+        systems_info = res.get("systems", {})
+
+        # Handle intmat.png
+        intmat_filename = os.path.join(self.output_dir, "intmat.png")
+        if os.path.exists(intmat_filename):
+            new_intmat_filename = os.path.join(
+                self.output_dir,
+                f"intmat_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.png"
+            )
+            os.rename(intmat_filename, new_intmat_filename)
+            self.job_info["intmat_plot"] = new_intmat_filename
+            self.log(f"intmat.png saved as {new_intmat_filename}")
+        else:
+            self.log("intmat.png not found.")
+
+        if "wads" in res:
+            # Save additional plots or data as needed
+            self.job_info["interface_scan_results"] = main_results_filename
+            self.job_info["w_adhesion"] = w_adhesion
+            self.job_info["systems_info"] = systems_info
+            self.log(f"Interface scan results saved to {main_results_filename}")
+            self.log(f"w_adhesion: {w_adhesion}")
+            self.log(f"systems_info: {systems_info}")
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+        else:
+            self.log(f"No 'wads' key in results file: {main_results_filename}")
+
+    def get_job_info_filename(self):
+        if hasattr(self, 'jid') and self.jid:
+            return os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            )
+        else:
+            return os.path.join(
+                self.output_dir, f"Interface_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}_job_info.json"
+            )
+
+    import numpy as np
+    import pandas as pd
+    from sklearn.metrics import mean_absolute_error, r2_score
+    import matplotlib.pyplot as plt
+    from matplotlib.gridspec import GridSpec
+    import os
+
+    def run_all(self):
+        """Run selected analyses based on configuration."""
+        # Start timing the entire run
+        start_time = time.time()
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for analysis.")
+            self.atoms = self.atoms.get_conventional_atoms
+        else:
+            self.atoms = self.atoms
+        # Relax the structure if specified
+        if 'relax_structure' in self.properties_to_calculate:
+            relaxed_atoms = self.relax_structure()
+        else:
+            relaxed_atoms = self.atoms
+
+        # Proceed only if the structure is relaxed or original atoms are used
+        if relaxed_atoms is None:
+            self.log("Relaxation did not converge. Exiting.")
+            return
+
+        if 'calculate_forces' in self.properties_to_calculate:
+            self.calculate_forces(self.atoms)
+
+        # Lattice parameters before and after relaxation
+        lattice_initial = self.atoms.lattice
+        lattice_final = relaxed_atoms.lattice
+
+        # Prepare final results dictionary
+        final_results = {}
+
+        # Initialize variables for error calculation
+        err_a = err_b = err_c = err_vol = err_form = err_kv = err_c11 = err_c44 = err_surf_en = err_vac_en = np.nan
+        form_en_entry = kv_entry = c11_entry = c44_entry = 0
+
+        # Calculate E-V curve and bulk modulus if specified
+        if 'calculate_ev_curve' in self.properties_to_calculate:
+            _, _, _, _, bulk_modulus, _, _ = self.calculate_ev_curve(relaxed_atoms)
+            kv_entry = self.reference_data.get("bulk_modulus_kv", 0)
+            final_results['modulus'] = {
+                'kv': bulk_modulus,
+                'kv_entry': kv_entry
+            }
+            err_kv = mean_absolute_error([kv_entry], [bulk_modulus]) if bulk_modulus is not None else np.nan
+
+                
+        # Formation energy
+        if 'calculate_formation_energy' in self.properties_to_calculate:
+            formation_energy = self.calculate_formation_energy(relaxed_atoms)
+            form_en_entry = self.reference_data.get("formation_energy_peratom", 0)
+            final_results['form_en'] = {
+                'form_energy': formation_energy,
+                'form_energy_entry': form_en_entry
+            }
+            err_form = mean_absolute_error([form_en_entry], [formation_energy])
+
+        # Elastic tensor
+        if 'calculate_elastic_tensor' in self.properties_to_calculate:
+            elastic_tensor = self.calculate_elastic_tensor(relaxed_atoms)
+            c11_entry = self.reference_data.get("elastic_tensor", [[0]])[0][0]
+            c44_entry = self.reference_data.get("elastic_tensor", [[0, 0, 0, [0, 0, 0, 0]]])[3][3]
+            final_results['elastic_tensor'] = {
+                'c11': elastic_tensor.get("C_11", 0),
+                'c44': elastic_tensor.get("C_44", 0),
+                'c11_entry': c11_entry,
+                'c44_entry': c44_entry
+            }
+            err_c11 = mean_absolute_error([c11_entry], [elastic_tensor.get("C_11", np.nan)])
+            err_c44 = mean_absolute_error([c44_entry], [elastic_tensor.get("C_44", np.nan)])
+
+        # Phonon analysis
+        if 'run_phonon_analysis' in self.properties_to_calculate:
+            phonon, zpe = self.run_phonon_analysis(relaxed_atoms)
+            final_results['zpe'] = zpe
+        else:
+            zpe = None
+
+        # Surface energy analysis
+        if 'analyze_surfaces' in self.properties_to_calculate:
+            self.analyze_surfaces()
+            surf_en, surf_en_entry = [], []
+            surface_entries = get_surface_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+
+            indices_list = self.surface_settings.get('indices_list', [
+                [1, 0, 0],
+                [1, 1, 1],
+                [1, 1, 0],
+                [0, 1, 1],
+                [0, 0, 1],
+                [0, 1, 0],
+            ])
+
+            for indices in indices_list:
+                surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+                calculated_surface_energy = self.job_info.get(surface_name, 0)
+                try:
+                    # Try to match the surface entry
+                    matching_entry = next(
+                        (entry for entry in surface_entries if entry['name'].strip() == surface_name.strip()),
+                        None
+                    )
+                    if matching_entry and calculated_surface_energy != 0 and matching_entry["surf_en_entry"] != 0:
+                        surf_en.append(calculated_surface_energy)
+                        surf_en_entry.append(matching_entry["surf_en_entry"])
+                    else:
+                        print(f"No valid matching entry found for {surface_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing surface {surface_name}: {e}")
+                    self.log(f"Error processing surface {surface_name}: {str(e)}")
+                    continue  # Skip this surface and move to the next one
+            final_results['surface_energy'] = [
+                {
+                    "name": f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}",
+                    "surf_en": se,
+                    "surf_en_entry": see
+                }
+                for se, see, indices in zip(surf_en, surf_en_entry, indices_list)
+            ]
+            err_surf_en = mean_absolute_error(surf_en_entry, surf_en) if surf_en else np.nan
+
+        # Vacancy energy analysis
+        if 'analyze_defects' in self.properties_to_calculate:
+            self.analyze_defects()
+            vac_en, vac_en_entry = [], []
+            vacancy_entries = get_vacancy_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+            for defect in Vacancy(self.atoms).generate_defects(on_conventional_cell=True, enforce_c_size=8, extend=1):
+                defect_name = f"{self.jid}_{defect.to_dict()['symbol']}"
+                vacancy_energy = self.job_info.get(f"vacancy_formation_energy for {defect_name}", 0)
+                try:
+                    # Try to match the vacancy entry
+                    matching_entry = next(
+                        (entry for entry in vacancy_entries if entry['symbol'] == defect_name),
+                        None
+                    )
+                    if matching_entry and vacancy_energy != 0 and matching_entry['vac_en_entry'] != 0:
+                        vac_en.append(vacancy_energy)
+                        vac_en_entry.append(matching_entry['vac_en_entry'])
+                    else:
+                        print(f"No valid matching entry found for {defect_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing defect {defect_name}: {e}")
+                    self.log(f"Error processing defect {defect_name}: {str(e)}")
+                    continue  # Skip this defect and move to the next one
+            final_results['vacancy_energy'] = [
+                {"name": ve_name, "vac_en": ve, "vac_en_entry": vee}
+                for ve_name, ve, vee in zip(
+                    [f"{self.jid}_{defect.to_dict()['symbol']}" for defect in Vacancy(self.atoms).generate_defects(
+                        on_conventional_cell=True, enforce_c_size=8, extend=1
+                    )],
+                    vac_en,
+                    vac_en_entry
+                )
+            ]
+            err_vac_en = mean_absolute_error(vac_en_entry, vac_en) if vac_en else np.nan
+
+        # Additional analyses
+        if 'analyze_interfaces' in self.properties_to_calculate and self.film_jid and self.substrate_jid:
+            self.analyze_interfaces()
+
+        if 'run_phonon3_analysis' in self.properties_to_calculate:
+            self.run_phonon3_analysis(relaxed_atoms)
+
+        if 'calculate_thermal_expansion' in self.properties_to_calculate:
+            self.calculate_thermal_expansion(relaxed_atoms)
+
+        if 'general_melter' in self.properties_to_calculate:
+            quenched_atoms = self.general_melter(relaxed_atoms)
+            if 'calculate_rdf' in self.properties_to_calculate:
+                self.calculate_rdf(quenched_atoms)
+
+        # Record lattice parameters
+        final_results['energy'] = {
+            "initial_a": lattice_initial.a,
+            "initial_b": lattice_initial.b,
+            "initial_c": lattice_initial.c,
+            "initial_vol": lattice_initial.volume,
+            "final_a": lattice_final.a,
+            "final_b": lattice_final.b,
+            "final_c": lattice_final.c,
+            "final_vol": lattice_final.volume,
+            "energy": self.job_info.get("final_energy_structure", 0)
+        }
+
+        # Error calculations
+        err_a = mean_absolute_error([lattice_initial.a], [lattice_final.a])
+        err_b = mean_absolute_error([lattice_initial.b], [lattice_final.b])
+        err_c = mean_absolute_error([lattice_initial.c], [lattice_final.c])
+        err_vol = mean_absolute_error([lattice_initial.volume], [lattice_final.volume])
+
+        # Create an error dictionary
+        error_dat = {
+            "err_a": err_a,
+            "err_b": err_b,
+            "err_c": err_c,
+            "err_form": err_form,
+            "err_vol": err_vol,
+            "err_kv": err_kv,
+            "err_c11": err_c11,
+            "err_c44": err_c44,
+            "err_surf_en": err_surf_en,
+            "err_vac_en": err_vac_en,
+            "time": time.time() - start_time
+        }
+
+        print("Error metrics calculated:", error_dat)
+
+        # Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+        # Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+        # Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        # Write results to a JSON file
+        output_file = os.path.join(self.output_dir, f"{self.jid}_{self.calculator_type}_results.json")
+        save_dict_to_json(final_results, output_file)
+
+        # Log total time
+        total_time = error_dat['time']
+        self.log(f"Total time for run: {total_time} seconds")
+
+        return error_dat
+
+
+# Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+# Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+# Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        return error_dat
+
+    def plot_error_scorecard(self, df):
+        import plotly.express as px
+
+        fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+        unique_dir = os.path.basename(self.output_dir)
+        fname_plot = os.path.join(self.output_dir, f"{unique_dir}_error_scorecard.png")
+        fig.write_image(fname_plot)
+        fig.show()
+
+def analyze_multiple_structures(jid_list, calculator_types, chemical_potentials_file):
+    composite_error_data = {}
+
+    for calculator_type in calculator_types:
+        # List to store individual error DataFrames
+        error_dfs = []
+
+        for jid in jid_list:
+            print(f"Analyzing {jid} with {calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                jid=jid,
+                calculator_type=calculator_type,
+                chemical_potentials_file=chemical_potentials_file,
+            )
+            # Run analysis and get error data
+            error_dat = analyzer.run_all()
+            error_df = pd.DataFrame([error_dat])
+            error_dfs.append(error_df)
+
+        # Concatenate all error DataFrames
+        all_errors_df = pd.concat(error_dfs, ignore_index=True)
+
+        # Compute composite errors by ignoring NaN values
+        composite_error = all_errors_df.mean(skipna=True).to_dict()
+
+        # Store the composite error data for this calculator type
+        composite_error_data[calculator_type] = composite_error
+
+    # Once all materials and calculators have been processed, create a DataFrame
+    composite_df = pd.DataFrame(composite_error_data).transpose()
+
+    # Plot the composite scorecard
+    plot_composite_scorecard(composite_df)
+
+    # Save the composite dataframe
+    composite_df.to_csv("composite_error_data.csv", index=True)
+
+def analyze_multiple_interfaces(film_jid_list, substrate_jid_list, calculator_types, chemical_potentials_file, film_index="1_1_0", substrate_index="1_1_0"):
+    for calculator_type in calculator_types:
+        for film_jid in film_jid_list:
+            for substrate_jid in substrate_jid_list:
+                print(f"Analyzing interface between {film_jid} and {substrate_jid} with {calculator_type}...")
+                analyzer = MaterialsAnalyzer(
+                    calculator_type=calculator_type,
+                    chemical_potentials_file=chemical_potentials_file,
+                    film_jid=film_jid,
+                    substrate_jid=substrate_jid,
+                    film_index=film_index,
+                    substrate_index=substrate_index,
+                )
+                analyzer.analyze_interfaces()
+
+def plot_composite_scorecard(df):
+    """Plot the composite scorecard for all calculators"""
+    fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+    fig.update_layout(title="Composite Scorecard for Calculators")
+    
+    # Save plot
+    fname_plot = "composite_error_scorecard.png"
+    fig.write_image(fname_plot)
+    fig.show()
+
+
+class MLearnForcesAnalyzer:
+    def __init__(self, calculator_type, mlearn_elements, output_dir=None):
+        self.calculator_type = calculator_type
+        self.mlearn_elements = mlearn_elements
+        elements_str = "_".join(self.mlearn_elements)
+        self.output_dir = output_dir or f"mlearn_analysis_{elements_str}_{calculator_type}"
+        os.makedirs(self.output_dir, exist_ok=True)
+        self.log_file = os.path.join(self.output_dir, "mlearn_analysis_log.txt")
+        self.setup_logger()
+        self.calculator = self.setup_calculator()
+        self.job_info = {
+            "calculator_type": calculator_type,
+            "mlearn_elements": mlearn_elements,
+        }
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger("MLearnForcesAnalyzer")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        self.logger.info(message)
+        print(message)
+
+    def setup_calculator(self):
+        return setup_calculator(self.calculator_type)
+
+    def run(self):
+        for element in self.mlearn_elements:
+            self.compare_mlearn_properties(element)
+
+    def compare_mlearn_properties(self, element):
+        """
+        Compare forces and stresses calculated by the FF calculator with mlearn DFT data for a given element.
+
+        Args:
+            element (str): Element symbol to filter structures (e.g., 'Si').
+        """
+        # Download the mlearn dataset if not already present
+        mlearn_zip_path = 'mlearn.json.zip'
+        if not os.path.isfile(mlearn_zip_path):
+            self.log("Downloading mlearn dataset...")
+            url = 'https://figshare.com/ndownloader/files/40357663'
+            response = requests.get(url)
+            with open(mlearn_zip_path, 'wb') as f:
+                f.write(response.content)
+            self.log("Download completed.")
+
+        # Read the JSON data from the zip file
+        with zipfile.ZipFile(mlearn_zip_path, 'r') as z:
+            with z.open('mlearn.json') as f:
+                mlearn_data = json.load(f)
+
+        # Convert mlearn data to DataFrame
+        df = pd.DataFrame(mlearn_data)
+
+        # Filter the dataset for the specified element
+        df['elements'] = df['atoms'].apply(lambda x: x['elements'])
+        df = df[df['elements'].apply(lambda x: element in x)]
+        df = df.reset_index(drop=True)
+        self.log(f"Filtered dataset to {len(df)} entries containing element '{element}'")
+
+        # Initialize lists to store results
+        force_results = []
+        stress_results = []
+
+        # Iterate over each structure
+        for idx, row in df.iterrows():
+            jid = row.get('jid', f"structure_{idx}")
+            atoms_dict = row['atoms']
+            atoms = Atoms.from_dict(atoms_dict)
+            dft_forces = np.array(row['forces'])
+            dft_stresses = np.array(row['stresses'])  # Original stresses in kBar
+
+            # Convert DFT stresses from kBar to GPa
+            dft_stresses_GPa = dft_stresses * -0.1  # kBar to GPa
+
+            # Convert DFT stresses to full 3x3 tensors
+            if dft_stresses_GPa.ndim == 1 and dft_stresses_GPa.size == 6:
+                dft_stress_tensor = voigt_6_to_full_3x3_stress(dft_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: DFT stresses not in expected format.")
+                continue  # Skip structures with unexpected stress format
+
+            # Calculate predicted properties
+            predicted_forces, predicted_stresses = self.calculate_properties(atoms)
+
+            # Convert predicted stresses from eV/ų to GPa
+            if predicted_stresses is not None and predicted_stresses.size == 6:
+                predicted_stresses_GPa = predicted_stresses * 160.21766208  # eV/ų to GPa
+                predicted_stress_tensor = voigt_6_to_full_3x3_stress(predicted_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: Predicted stresses not available.")
+                continue  # Skip structures where stresses are not available
+
+            # Flatten the 3x3 stress tensors to 9-component arrays for comparison
+            dft_stress_flat = dft_stress_tensor.flatten()
+            predicted_stress_flat = predicted_stress_tensor.flatten()
+
+            # Store the results
+            force_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_forces.flatten())),
+                'prediction': ';'.join(map(str, predicted_forces.flatten())),
+            })
+            stress_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_stress_flat)),
+                'prediction': ';'.join(map(str, predicted_stress_flat)),
+            })
+
+            # Optional: Progress indicator
+            if idx % 10 == 0:
+                self.log(f"Processed {idx + 1}/{len(df)} structures.")
+
+        # Ensure we have data to process
+        if not force_results or not stress_results:
+            self.log("No valid data found for forces or stresses. Exiting.")
+            return
+
+        # Save results to CSV files
+        force_df = pd.DataFrame(force_results)
+        force_csv = os.path.join(self.output_dir, f'AI-MLFF-forces-mlearn_{element}-test-multimae.csv')
+        force_df.to_csv(force_csv, index=False)
+        self.log(f"Saved force comparison data to '{force_csv}'")
+
+        stress_df = pd.DataFrame(stress_results)
+        stress_csv = os.path.join(self.output_dir, f'AI-MLFF-stresses-mlearn_{element}-test-multimae.csv')
+        stress_df.to_csv(stress_csv, index=False)
+        self.log(f"Saved stress comparison data to '{stress_csv}'")
+
+        # Zip the CSV files
+        self.zip_file(force_csv)
+        self.zip_file(stress_csv)
+
+        # Calculate error metrics
+        # Forces MAE
+        target_forces = np.concatenate(force_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_forces = np.concatenate(force_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        forces_mae = mean_absolute_error(target_forces, pred_forces)
+        self.log(f"Forces MAE for element '{element}': {forces_mae:.6f} eV/Å")
+
+        # Stresses MAE
+        target_stresses = np.concatenate(stress_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_stresses = np.concatenate(stress_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        stresses_mae = mean_absolute_error(target_stresses, pred_stresses)
+        self.log(f"Stresses MAE for element '{element}': {stresses_mae:.6f} GPa")
+
+        # Save MAE to job_info
+        self.job_info[f'forces_mae_{element}'] = forces_mae
+        self.job_info[f'stresses_mae_{element}'] = stresses_mae
+        self.save_job_info()
+
+        # Plot parity plots
+        forces_plot_filename = os.path.join(self.output_dir, f'forces_parity_plot_{element}.png')
+        self.plot_parity(target_forces, pred_forces, 'Forces', 'eV/Å', forces_plot_filename, element)
+
+        stresses_plot_filename = os.path.join(self.output_dir, f'stresses_parity_plot_{element}.png')
+        self.plot_parity(target_stresses, pred_stresses, 'Stresses', 'GPa', stresses_plot_filename, element)
+
+    def calculate_properties(self, atoms):
+        """
+        Calculate forces and stresses on the given atoms.
+
+        Returns:
+            Tuple of forces and stresses.
+        """
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Calculate properties
+        forces = ase_atoms.get_forces()
+        stresses = ase_atoms.get_stress()  # Voigt 6-component stress
+
+        return forces, stresses  # Return forces and stresses in Voigt notation
+
+    def plot_parity(self, target, prediction, property_name, units, filename, element):
+        """
+        Plot parity plot for a given property.
+
+        Args:
+            target (array-like): Target values.
+            prediction (array-like): Predicted values.
+            property_name (str): Name of the property (e.g., 'Forces').
+            units (str): Units of the property (e.g., 'eV/Å' or 'GPa').
+            filename (str): Filename to save the plot.
+            element (str): Element symbol.
+        """
+        plt.figure(figsize=(8, 8), dpi=300)
+        plt.scatter(target, prediction, alpha=0.5, edgecolors='k', s=20)
+        min_val = min(np.min(target), np.min(prediction))
+        max_val = max(np.max(target), np.max(prediction))
+        plt.plot([min_val, max_val], [min_val, max_val], 'k--', lw=2)
+        plt.xlabel(f'Target {property_name} ({units})', fontsize=14)
+        plt.ylabel(f'Predicted {property_name} ({units})', fontsize=14)
+        plt.title(f'Parity Plot for {property_name} - Element {element}', fontsize=16)
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(filename)
+        plt.close()
+        self.log(f"Saved parity plot for {property_name} as '{filename}'")
+
+    def zip_file(self, filename):
+        zip_filename = filename + '.zip'
+        with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
+            zf.write(filename, arcname=os.path.basename(filename))
+        os.remove(filename)  # Remove the original file
+        self.log(f"Zipped data to '{zip_filename}'")
+
+    def save_job_info(self):
+        job_info_filename = os.path.join(self.output_dir, f"mlearn_{self.calculator_type}_job_info.json")
+        with open(job_info_filename, 'w') as f:
+            json.dump(self.job_info, f, indent=4)
+
+
+#jid_list=['JVASP-1002']
+jid_list_all = [ 'JVASP-1002', 'JVASP-816', 'JVASP-867', 'JVASP-1029', 'JVASP-861','JVASP-30', 'JVASP-8169', 'JVASP-890', 'JVASP-8158','JVASP-8118',
+    'JVASP-107', 'JVASP-39', 'JVASP-7844', 'JVASP-35106', 'JVASP-1174',
+    'JVASP-1372', 'JVASP-91', 'JVASP-1186', 'JVASP-1408', 'JVASP-105410',
+    'JVASP-1177', 'JVASP-79204', 'JVASP-1393', 'JVASP-1312', 'JVASP-1327',
+    'JVASP-1183', 'JVASP-1192', 'JVASP-8003', 'JVASP-96', 'JVASP-1198',
+    'JVASP-1195', 'JVASP-9147', 'JVASP-41', 'JVASP-34674', 'JVASP-113',
+    'JVASP-32', 'JVASP-840', 'JVASP-21195', 'JVASP-981', 'JVASP-969',
+    'JVASP-802', 'JVASP-943', 'JVASP-14812', 'JVASP-984', 'JVASP-972',
+    'JVASP-958', 'JVASP-901', 'JVASP-1702', 'JVASP-931', 'JVASP-963',
+    'JVASP-95', 'JVASP-1201', 'JVASP-14837', 'JVASP-825', 'JVASP-966',
+    'JVASP-993', 'JVASP-23', 'JVASP-828', 'JVASP-1189', 'JVASP-810',
+    'JVASP-7630', 'JVASP-819', 'JVASP-1180', 'JVASP-837', 'JVASP-919',
+    'JVASP-7762', 'JVASP-934', 'JVASP-858', 'JVASP-895']
+#calculator_types = ["alignn_ff_aff307k_lmdb_param_low_rad_use_force_mult_mp_tak4","alignn_ff_v5.27.2024","alignn_ff_aff307k_kNN_2_2_128"]
+
+if __name__ == "__main__":
+    import pprint
+    parser = argparse.ArgumentParser(description="Run Materials Analyzer")
+    parser.add_argument("--input_file", default="input.json", type=str, help="Path to the input configuration JSON file")
+    args = parser.parse_args()
+
+    input_file = loadjson(args.input_file)
+    input_file_data = CHIPSFFConfig(**input_file)
+    pprint.pprint(input_file_data.dict())
+
+    # If film_id is provided, treat it as a list
+    film_jids = input_file_data.film_id if input_file_data.film_id else []
+
+    # If substrate_id is provided, treat it as a list
+    substrate_jids = input_file_data.substrate_id if input_file_data.substrate_id else []
+
+    # Case 1: Interface calculations with film_jid and substrate_jid
+    if film_jids and substrate_jids:
+        # Loop through all film and substrate JIDs and perform interface analysis
+        for film_jid, substrate_jid in zip(film_jids, substrate_jids):
+            print(f"Analyzing interface between {film_jid} and {substrate_jid} with {input_file_data.calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                calculator_type=input_file_data.calculator_type,
+                chemical_potentials_file=input_file_data.chemical_potentials_file,
+                film_jid=film_jid,
+                substrate_jid=substrate_jid,
+                film_index=input_file_data.film_index,
+                substrate_index=input_file_data.substrate_index,
+                bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+                phonon_settings=input_file_data.phonon_settings,
+                properties_to_calculate=input_file_data.properties_to_calculate,
+            )
+            analyzer.analyze_interfaces()
+
+    # Case 2: Single JID provided
+    elif input_file_data.jid and input_file_data.calculator_type:
+        print(f"Analyzing {input_file_data.jid} with {input_file_data.calculator_type}...")
+        analyzer = MaterialsAnalyzer(
+            jid=input_file_data.jid,
+            calculator_type=input_file_data.calculator_type,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+        analyzer.run_all()
+
+    # Case 3: Multiple JIDs and calculator types provided (batch processing)
+    elif input_file_data.jid_list and input_file_data.calculator_types:
+        analyze_multiple_structures(
+            jid_list=input_file_data.jid_list,
+            calculator_types=input_file_data.calculator_types,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+
+    elif input_file_data.mlearn_elements and input_file_data.calculator_type:
+        print(f"Running mlearn forces comparison for elements {input_file_data.mlearn_elements} with {input_file_data.calculator_type}...")
+        mlearn_analyzer = MLearnForcesAnalyzer(
+            calculator_type=input_file_data.calculator_type,
+            mlearn_elements=input_file_data.mlearn_elements
+        )
+        mlearn_analyzer.run()
+    else:
+        print("Please provide valid arguments in the configuration file.")
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/AI-MLFF-forces-mlearnall_Li-test-multimae.csv.zip b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Li-test-multimae.csv.zip
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/AI-MLFF-forces-mlearnall_Li-test-multimae.csv.zip
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Li-test-multimae.csv.zip
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/metadata.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/metadata.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/metadata.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/mlearn_alignn_ff_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/mlearn_alignn_ff_job_info.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/mlearn_analysis_log.txt
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/mlearn_analysis_log.txt
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/mlearn_analysis_log.txt
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/run.sh
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/run.sh
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/run.sh
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/run_chipsff.py
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff/run_chipsff.py
rename to jarvis_leaderboard/contributions/mlearn_analysis_Li_alignn_ff_5_27_24/run_chipsff.py
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diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/metadata.json
new file mode 100644
index 000000000..93f5c9fd0
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/metadata.json
@@ -0,0 +1,15 @@
+{
+    "model_name": "CHIPS-FF",
+    "project_url": "https://github.com/usnistgov/chipsff",
+    "date_submitted": "11-22-2024",
+    "author_email": "daniel.wines@nist.gov",
+    "time_taken_seconds": {},
+    "team_name": "JARVIS",
+    "language": "python",
+    "os": "linux",
+    "software_used": "jarvis-tools,numpy,scipy,torch,alignn",
+    "hardware_used": "mr-french at NIST",
+    "git_url": [
+        "https://github.com/usnistgov/chipsff"
+    ]
+}
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
new file mode 100644
index 000000000..c9cd61e14
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
@@ -0,0 +1,8 @@
+{
+    "calculator_type": "alignn_ff_12_2_24",
+    "mlearn_elements": [
+        "Mo"
+    ],
+    "forces_mae_Mo": 0.8687823188310289,
+    "stresses_mae_Mo": 5.529467208325023
+}
\ No newline at end of file
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/mlearn_analysis_log.txt
new file mode 100644
index 000000000..b1e3eb060
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/mlearn_analysis_log.txt
@@ -0,0 +1,31 @@
+2024-12-03 16:35:08,320 - INFO - Filtered dataset to 217 entries containing element 'Mo'
+2024-12-03 16:35:09,997 - INFO - Processed 1/217 structures.
+2024-12-03 16:35:15,335 - INFO - Processed 11/217 structures.
+2024-12-03 16:35:21,398 - INFO - Processed 21/217 structures.
+2024-12-03 16:35:27,153 - INFO - Processed 31/217 structures.
+2024-12-03 16:35:32,823 - INFO - Processed 41/217 structures.
+2024-12-03 16:35:38,458 - INFO - Processed 51/217 structures.
+2024-12-03 16:35:44,255 - INFO - Processed 61/217 structures.
+2024-12-03 16:35:50,052 - INFO - Processed 71/217 structures.
+2024-12-03 16:35:56,290 - INFO - Processed 81/217 structures.
+2024-12-03 16:36:02,325 - INFO - Processed 91/217 structures.
+2024-12-03 16:36:09,036 - INFO - Processed 101/217 structures.
+2024-12-03 16:36:15,920 - INFO - Processed 111/217 structures.
+2024-12-03 16:36:22,901 - INFO - Processed 121/217 structures.
+2024-12-03 16:36:28,847 - INFO - Processed 131/217 structures.
+2024-12-03 16:36:32,234 - INFO - Processed 141/217 structures.
+2024-12-03 16:36:37,249 - INFO - Processed 151/217 structures.
+2024-12-03 16:36:41,447 - INFO - Processed 161/217 structures.
+2024-12-03 16:36:45,940 - INFO - Processed 171/217 structures.
+2024-12-03 16:36:49,978 - INFO - Processed 181/217 structures.
+2024-12-03 16:36:54,062 - INFO - Processed 191/217 structures.
+2024-12-03 16:36:58,221 - INFO - Processed 201/217 structures.
+2024-12-03 16:37:01,613 - INFO - Processed 211/217 structures.
+2024-12-03 16:37:04,120 - INFO - Saved force comparison data to 'mlearn_analysis_Mo_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Mo-test-multimae.csv'
+2024-12-03 16:37:04,128 - INFO - Saved stress comparison data to 'mlearn_analysis_Mo_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Mo-test-multimae.csv'
+2024-12-03 16:37:04,366 - INFO - Zipped data to 'mlearn_analysis_Mo_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Mo-test-multimae.csv.zip'
+2024-12-03 16:37:04,379 - INFO - Zipped data to 'mlearn_analysis_Mo_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Mo-test-multimae.csv.zip'
+2024-12-03 16:37:04,454 - INFO - Forces MAE for element 'Mo': 0.868782 eV/Å
+2024-12-03 16:37:04,471 - INFO - Stresses MAE for element 'Mo': 5.529467 GPa
+2024-12-03 16:37:07,572 - INFO - Saved parity plot for Forces as 'mlearn_analysis_Mo_alignn_ff_12_2_24/forces_parity_plot_Mo.png'
+2024-12-03 16:37:09,274 - INFO - Saved parity plot for Stresses as 'mlearn_analysis_Mo_alignn_ff_12_2_24/stresses_parity_plot_Mo.png'
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/run.sh
new file mode 100644
index 000000000..0e331ca03
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/run.sh
@@ -0,0 +1,148 @@
+#!/bin/bash
+
+# Create logs directory if it doesn't exist
+mkdir -p logs
+
+# Define arrays of JIDs, calculators, and mlearn elements
+calculator_types=("alignn_ff_12_2_24")
+mlearn_elements=("Si" "Ge" "Cu" "Ni" "Li" "Mo")
+
+# Function to submit a job for bulk material calculations
+submit_bulk_job() {
+  local jid=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=30-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=${jid}_${calculator}
+#SBATCH --output=logs/${jid}_${calculator}_%j.out
+#SBATCH --error=logs/${jid}_${calculator}_%j.err
+
+# Generate input JSON file for this combination
+cat > input_${jid}_${calculator}.json <<JSON
+{
+  "jid": "$jid",
+  "calculator_type": "$calculator",
+  "chemical_potentials_file": "chemical_potentials.json",
+  "properties_to_calculate": [
+    "relax_structure",
+    "calculate_ev_curve",
+    "calculate_formation_energy",
+    "calculate_elastic_tensor",
+    "run_phonon_analysis",
+    "analyze_surfaces",
+    "analyze_defects",
+    "run_phonon3_analysis",
+    "general_melter",
+    "calculate_rdf"
+  ],
+  "bulk_relaxation_settings": {
+    "filter_type": "ExpCellFilter",
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": false
+    }
+  },
+  "phonon_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "use_conventional_cell": true,
+  "surface_settings": {
+    "indices_list": [
+      [0, 1, 0],
+      [0, 0, 1]
+    ],
+    "layers": 4,
+    "vacuum": 18,
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "defect_settings": {
+    "generate_settings": {
+      "on_conventional_cell": true,
+      "enforce_c_size": 8,
+      "extend": 1
+    },
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "phonon3_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "md_settings": {
+    "dt": 1,
+    "temp0": 35,
+    "nsteps0": 10,
+    "temp1": 200,
+    "nsteps1": 20,
+    "taut": 20,
+    "min_size": 10.0
+  }
+}
+JSON
+
+# Run the Python analysis for this JID/calculator combination
+python run_chipsff.py --input_file input_${jid}_${calculator}.json
+
+EOT
+}
+
+# Function to submit a job for mlearn analysis
+submit_mlearn_job() {
+  local element=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=1-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=mlearn_${element}_${calculator}
+#SBATCH --output=logs/mlearn_${element}_${calculator}_%j.out
+#SBATCH --error=logs/mlearn_${element}_${calculator}_%j.err
+
+# Generate input JSON file for this mlearn element/calculator combination
+cat > input_mlearn_${element}_${calculator}.json <<JSON
+{
+  "mlearn_elements": ["$element"],
+  "calculator_type": "$calculator",
+  "properties_to_calculate": ["compare_mlearn_forces"]
+}
+JSON
+
+# Run the Python analysis for this mlearn element/calculator combination
+python run_chipsff.py --input_file input_mlearn_${element}_${calculator}.json
+
+EOT
+}
+
+# Loop through each JID and calculator combination for bulk calculations
+for jid in "${jid_list[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_bulk_job "$jid" "$calculator"
+  done
+done
+
+# Loop through each mlearn element and calculator combination for mlearn analysis
+for element in "${mlearn_elements[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_mlearn_job "$element" "$calculator"
+  done
+done
+
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/run_chipsff.py
new file mode 100644
index 000000000..79b65eb07
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_12_2_24/run_chipsff.py
@@ -0,0 +1,2286 @@
+#!/usr/bin/env python
+import os
+import subprocess
+import sys
+import time
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from ase.eos import EquationOfState
+from ase import Atoms as AseAtoms
+from ase.units import kJ
+from ase.constraints import ExpCellFilter
+from ase.optimize.fire import FIRE
+from ase.stress import voigt_6_to_full_3x3_stress
+import ase.units
+from jarvis.db.figshare import get_jid_data, data, get_request_data
+from jarvis.core.atoms import Atoms, ase_to_atoms
+from jarvis.io.vasp.inputs import Poscar
+from jarvis.core.kpoints import Kpoints3D as Kpoints
+from jarvis.analysis.defects.vacancy import Vacancy
+from jarvis.analysis.defects.surface import Surface
+import pandas as pd
+import h5py
+import shutil
+import glob
+import io
+import contextlib
+import requests
+import zipfile
+import re
+import plotly.express as px
+from sklearn.metrics import mean_absolute_error, r2_score
+from matplotlib.gridspec import GridSpec
+import argparse
+from jarvis.db.jsonutils import loadjson
+from chipsff.config import CHIPSFFConfig
+
+dft_3d = data("dft_3d")
+vacancydb = data("vacancydb")
+surf_url = "https://figshare.com/ndownloader/files/46355689"
+surface_data = get_request_data(js_tag="surface_db_dd.json", url=surf_url)
+
+def get_entry(jid):
+    for entry in dft_3d:
+        if entry["jid"] == jid:
+            return entry
+    raise ValueError(f"JID {jid} not found in the database")
+
+def collect_data(dft_3d, vacancydb, surface_data):
+    defect_ids = list(set([entry["jid"] for entry in vacancydb]))
+    surf_ids = list(set([entry["name"].split("Surface-")[1].split("_miller_")[0] for entry in surface_data]))
+
+    aggregated_data = []
+    for entry in dft_3d:
+        tmp = entry
+        tmp["vacancy"] = {}
+        tmp["surface"] = {}
+
+        # Check if the entry is in the defect dataset
+        if entry["jid"] in defect_ids:
+            for vac_entry in vacancydb:
+                if entry["jid"] == vac_entry["jid"]:
+                    tmp["vacancy"].setdefault(
+                        vac_entry["id"].split("_")[0] + "_" + vac_entry["id"].split("_")[1],
+                        vac_entry["ef"]
+                    )
+
+        # Check if the entry is in the surface dataset
+        if entry["jid"] in surf_ids:
+            for surf_entry in surface_data:
+                jid = surf_entry["name"].split("Surface-")[1].split("_miller_")[0]
+                if entry["jid"] == jid:
+                    tmp["surface"].setdefault(
+                        "_".join(surf_entry["name"].split("_thickness")[0].split("_")[0:5]),
+                        surf_entry["surf_en"]
+                    )
+
+        aggregated_data.append(tmp)
+    
+    return aggregated_data
+
+def get_vacancy_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the vacancy formation energy entry (vac_en_entry) for a given jid.
+    
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+    
+    Returns:
+    dict: A dictionary containing the vacancy formation energy entry and corresponding symbol.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            vacancy_data = entry.get("vacancy", {})
+            if vacancy_data:
+                return [{"symbol": key, "vac_en_entry": value} for key, value in vacancy_data.items()]
+            else:
+                return f"No vacancy data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+    
+def get_surface_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the surface energy entry (surf_en_entry) for a given jid.
+
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+
+    Returns:
+    list: A list of dictionaries containing the surface energy entry and corresponding name.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            surface_data = entry.get("surface", {})
+            if surface_data:
+                # Prepend 'Surface-JVASP-<jid>_' to the key for correct matching
+                return [{"name": f"{key}", "surf_en_entry": value} for key, value in surface_data.items()]
+            else:
+                return f"No surface data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+
+
+def log_job_info(message, log_file):
+    """Log job information to a file and print it."""
+    with open(log_file, "a") as f:
+        f.write(message + "\n")
+    print(message)
+
+def save_dict_to_json(data_dict, filename):
+    with open(filename, "w") as f:
+        json.dump(data_dict, f, indent=4)
+
+def load_dict_from_json(filename):
+    with open(filename, 'r') as f:
+        return json.load(f)
+        
+def setup_calculator(calculator_type):
+    if calculator_type == "matgl":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "matgl-direct":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-DIRECT-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "alignn_ff":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+        model_path = default_path() #can be adjusted to other ALIGNN models
+        return AlignnAtomwiseCalculator(
+            path=model_path,
+            stress_wt=0.1,
+            force_mult_natoms=False,
+            force_multiplier=1,
+            modl_filename="best_model.pt",
+        )
+    elif calculator_type == "alignn_ff_12_2_24":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+        return AlignnAtomwiseCalculator()
+    elif calculator_type == "chgnet":
+        from chgnet.model.dynamics import CHGNetCalculator
+        return CHGNetCalculator()
+    elif calculator_type == "mace":
+        from mace.calculators import mace_mp
+        return mace_mp()
+    elif calculator_type == "mace-alexandria":
+        from mace.calculators.mace import MACECalculator
+        model_path="/users/dtw2/utils/models/alexandria_v2/mace/2D_universal_force_field_cpu.model" #adjust path to mace-alexandria
+        return MACECalculator(model_path,device="cpu") 
+    elif calculator_type == "sevennet":
+        from sevenn.sevennet_calculator import SevenNetCalculator
+        checkpoint_path = "/users/dtw2/SevenNet/pretrained_potentials/SevenNet_0__11July2024/checkpoint_sevennet_0.pth" #adjust path to sevennet
+        return SevenNetCalculator(checkpoint_path, device="cpu")
+    elif calculator_type == "orb-v2":
+        from orb_models.forcefield import pretrained
+        from orb_models.forcefield.calculator import ORBCalculator
+        orbff = pretrained.orb_v2()
+        return ORBCalculator(orbff, device="cpu")
+    elif calculator_type == "eqV2_31M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat.pt") #adjust path to OMat24   
+    elif calculator_type == "eqV2_153M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_153M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_31M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat_mp_salex.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat_mp_salex.pt") #adjust path to OMat24
+    else:
+        raise ValueError("Unsupported calculator type")
+
+class MaterialsAnalyzer:
+    def __init__(
+        self,
+        jid=None,
+        calculator_type=None,
+        chemical_potentials_file=None,
+        film_jid=None,
+        substrate_jid=None,
+        film_index=None,
+        substrate_index=None,
+        bulk_relaxation_settings=None,
+        phonon_settings=None,
+        properties_to_calculate=None,
+        use_conventional_cell=False,
+        surface_settings=None,
+        defect_settings=None,
+        phonon3_settings=None,
+        md_settings=None
+    ):
+        self.calculator_type = calculator_type
+        self.use_conventional_cell = use_conventional_cell
+        self.chemical_potentials_file = chemical_potentials_file
+        self.bulk_relaxation_settings = bulk_relaxation_settings or {}
+        self.phonon_settings = phonon_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.properties_to_calculate = properties_to_calculate or []
+        self.surface_settings = surface_settings or {}
+        self.defect_settings = defect_settings or {}
+        self.film_index = film_index or "1_1_0"
+        self.substrate_index = substrate_index or "1_1_0"
+        self.phonon3_settings = phonon3_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.md_settings = md_settings or {'dt': 1, 'temp0': 3500, 'nsteps0': 1000, 'temp1': 300, 'nsteps1': 2000, 'taut': 20, 'min_size': 10.0}
+        if jid:
+            self.jid = jid
+            # Load atoms for the given JID
+            self.atoms = self.get_atoms(jid)
+            # Get reference data for the material
+            self.reference_data = get_entry(jid)
+            # Set up the output directory and log file
+            self.output_dir = f"{jid}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(self.output_dir, f"{jid}_job_log.txt")
+            # Initialize job_info dictionary
+            self.job_info = {
+                "jid": jid,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        elif film_jid and substrate_jid:
+            # Ensure film_jid and substrate_jid are strings, not lists
+            if isinstance(film_jid, list):
+                film_jid = film_jid[0]
+            if isinstance(substrate_jid, list):
+                substrate_jid = substrate_jid[0]
+
+            self.film_jid = film_jid
+            self.substrate_jid = substrate_jid
+
+            # Include Miller indices in directory and file names
+            self.output_dir = f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(
+                self.output_dir,
+                f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_job_log.txt",
+            )
+            self.job_info = {
+                "film_jid": film_jid,
+                "substrate_jid": substrate_jid,
+                "film_index": self.film_index,
+                "substrate_index": self.substrate_index,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        else:
+            raise ValueError("Either 'jid' or both 'film_jid' and 'substrate_jid' must be provided.")
+
+        # Set up the logger
+        self.setup_logger()
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger(self.jid or f"{self.film_jid}_{self.substrate_jid}")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        """Log information to the job log file."""
+        log_job_info(message, self.log_file)
+
+    def get_atoms(self, jid):
+        dat = get_jid_data(jid=jid, dataset="dft_3d")
+        return Atoms.from_dict(dat["atoms"])
+
+    def setup_calculator(self):
+        calc = setup_calculator(self.calculator_type)
+        self.log(f"Using calculator: {self.calculator_type}")
+        return calc
+
+    def load_chemical_potentials(self):
+        if os.path.exists(self.chemical_potentials_file):
+            with open(self.chemical_potentials_file, "r") as f:
+                return json.load(f)
+        else:
+            return {}
+
+    def save_chemical_potentials(self):
+        with open(self.chemical_potentials_file, "w") as f:
+            json.dump(self.chemical_potentials, f, indent=4)
+
+    def capture_fire_output(self, ase_atoms, fmax, steps):
+        """Capture the output of the FIRE optimizer."""
+        log_stream = io.StringIO()
+        with contextlib.redirect_stdout(log_stream):
+            dyn = FIRE(ase_atoms)
+            dyn.run(fmax=fmax, steps=steps)
+        output = log_stream.getvalue().strip()
+
+        final_energy = None
+        if output:
+            last_line = output.split("\n")[-1]
+            match = re.search(r"FIRE:\s+\d+\s+\d+:\d+:\d+\s+(-?\d+\.\d+)", last_line)
+            if match:
+                final_energy = float(match.group(1))
+
+        return final_energy, dyn.nsteps
+
+    def relax_structure(self):
+        """Perform structure relaxation and log the process."""
+        self.log(f"Starting relaxation for {self.jid}")
+
+        # Use conventional cell if specified
+
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for relaxation.")
+            self.atoms = self.atoms.get_conventional_atoms  # or appropriate method
+
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.bulk_relaxation_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.bulk_relaxation_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', False)
+        ase_atoms = self.atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+
+        # Run FIRE optimizer and capture the output using relaxation settings
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < steps
+
+        # Log the final energy and relaxation status
+        self.log(f"Final energy of FIRE optimization for structure: {final_energy}")
+        self.log(
+            f"Relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        # Update job info and save the relaxed structure
+        self.job_info["relaxed_atoms"] = relaxed_atoms.to_dict()
+        self.job_info["final_energy_structure"] = final_energy
+        self.job_info["converged"] = converged
+        self.log(f"Relaxed structure: {relaxed_atoms.to_dict()}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return relaxed_atoms if converged else None
+
+
+
+    def calculate_formation_energy(self, relaxed_atoms):
+        """
+        Calculate the formation energy per atom using the equilibrium energy and chemical potentials.
+        """
+        e0 = self.job_info["equilibrium_energy"]
+        composition = relaxed_atoms.composition.to_dict()
+        total_energy = e0
+
+        for element, amount in composition.items():
+            chemical_potential = self.get_chemical_potential(element)
+            if chemical_potential is None:
+                self.log(f"Skipping formation energy calculation due to missing chemical potential for {element}.")
+                continue  # Or handle this appropriately
+            total_energy -= chemical_potential * amount
+
+        formation_energy_per_atom = total_energy / relaxed_atoms.num_atoms
+
+    # Log and save the formation energy
+        self.job_info["formation_energy_per_atom"] = formation_energy_per_atom
+        self.log(f"Formation energy per atom: {formation_energy_per_atom}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return formation_energy_per_atom
+
+    def calculate_element_chemical_potential(self, element, element_jid):
+        """
+        Calculate the chemical potential of a pure element using its standard structure.
+        """
+        self.log(f"Calculating chemical potential for element: {element} using JID: {element_jid}")
+        try:
+            # Get standard structure for the element using the provided JID
+            element_atoms = self.get_atoms(element_jid)
+            ase_atoms = element_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+
+            # Perform energy calculation
+            energy = ase_atoms.get_potential_energy() / len(ase_atoms)
+            self.log(f"Calculated chemical potential for {element}: {energy} eV/atom")
+            return energy
+        except Exception as e:
+            self.log(f"Error calculating chemical potential for {element}: {e}")
+            return None
+
+    def get_chemical_potential(self, element):
+        """Fetch chemical potential from JSON based on the element and calculator."""
+        element_data = self.chemical_potentials.get(element, {})
+        chemical_potential = element_data.get(f"energy_{self.calculator_type}")
+
+        if chemical_potential is None:
+            self.log(
+                f"No chemical potential found for {element} with calculator {self.calculator_type}, calculating it now..."
+            )
+            # Get standard JID for the element from chemical_potentials.json
+            element_jid = element_data.get("jid")
+            if element_jid is None:
+                self.log(f"No standard JID found for element {element} in chemical_potentials.json")
+                return None  # Skip this element
+
+            # Calculate chemical potential
+            chemical_potential = self.calculate_element_chemical_potential(element, element_jid)
+            if chemical_potential is None:
+                self.log(f"Failed to calculate chemical potential for {element}")
+                return None
+            # Add it to the chemical potentials dictionary
+            if element not in self.chemical_potentials:
+                self.chemical_potentials[element] = {}
+            self.chemical_potentials[element][f"energy_{self.calculator_type}"] = chemical_potential
+            # Save the updated chemical potentials to file
+            self.save_chemical_potentials()
+
+        return chemical_potential
+    def calculate_forces(self, atoms):
+        """
+        Calculate the forces on the given atoms without performing relaxation.
+        """
+        self.log(f"Calculating forces for {self.jid}")
+
+    # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+    # Calculate forces
+        forces = ase_atoms.get_forces()  # This returns an array of forces
+
+    # Log and save the forces
+        self.job_info['forces'] = forces.tolist()  # Convert to list for JSON serialization
+        self.log(f"Forces calculated: {forces}")
+
+    # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return forces
+
+    def calculate_ev_curve(self, relaxed_atoms):
+        """Calculate the energy-volume (E-V) curve and log results."""
+        self.log(f"Calculating EV curve for {self.jid}")
+
+        dx = np.arange(-0.06, 0.06, 0.01)  # Strain values
+        y = []  # Energies
+        vol = []  # Volumes
+        strained_structures = []  # To store strained structures
+
+        for i in dx:
+            # Apply strain and calculate energy at each strain value
+            strained_atoms = relaxed_atoms.strain_atoms(i)
+            strained_structures.append(strained_atoms)
+            ase_atoms = strained_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+            energy = ase_atoms.get_potential_energy()
+
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+        # Convert data to numpy arrays for processing
+        y = np.array(y)
+        vol = np.array(vol)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        try:
+            eos = EquationOfState(vol, y, eos="murnaghan")
+            v0, e0, B = eos.fit()
+
+            # Bulk modulus in GPa (conversion factor from ASE units)
+            kv = B / kJ * 1.0e24  # Convert to GPa
+
+            # Log important results
+            self.log(f"Bulk modulus: {kv} GPa")
+            self.log(f"Equilibrium energy: {e0} eV")
+            self.log(f"Equilibrium volume: {v0} ų")
+
+            # Save E-V curve plot
+            fig = plt.figure()
+            eos.plot()
+            ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+            fig.savefig(ev_plot_filename)
+            plt.close(fig)
+            self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+            # Save E-V curve data to a text file
+            ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+            with open(ev_data_filename, "w") as f:
+                f.write("Volume (ų)\tEnergy (eV)\n")
+                for v, e in zip(vol, y):
+                    f.write(f"{v}\t{e}\n")
+            self.log(f"E-V curve data saved to {ev_data_filename}")
+
+            # Update job info with the results
+            self.job_info["bulk_modulus"] = kv
+            self.job_info["equilibrium_energy"] = e0
+            self.job_info["equilibrium_volume"] = v0
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        except RuntimeError as e:
+            self.log(f"Error fitting EOS for {self.jid}: {e}")
+            self.log("Skipping bulk modulus calculation due to fitting error.")
+            kv = None  # Set bulk modulus to None or handle this as you wish
+            e0, v0 = None, None  # Set equilibrium energy and volume to None
+
+        # Return additional values for thermal expansion analysis
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+
+    def calculate_elastic_tensor(self, relaxed_atoms):
+        import elastic
+        from elastic import get_elementary_deformations, get_elastic_tensor
+        """
+        Calculate the elastic tensor for the relaxed structure using the provided calculator.
+        """
+        self.log(f"Starting elastic tensor calculation for {self.jid}")
+        start_time = time.time()
+
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Get elementary deformations for elastic tensor calculation
+        systems = elastic.get_elementary_deformations(ase_atoms)
+
+        # Calculate the elastic tensor and convert to GPa
+        cij_order = elastic.elastic.get_cij_order(ase_atoms)
+        Cij, Bij = elastic.get_elastic_tensor(ase_atoms, systems)
+        elastic_tensor = {
+            i: j / ase.units.GPa for i, j in zip(cij_order, Cij)
+        }  # Convert to GPa
+
+        # Save and log the results
+        self.job_info["elastic_tensor"] = elastic_tensor
+        self.log(
+            f"Elastic tensor for {self.jid} with {self.calculator_type}: {elastic_tensor}"
+        )
+
+        # Timing the calculation
+        end_time = time.time()
+        self.log(f"Elastic Calculation time: {end_time - start_time} seconds")
+
+        # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return elastic_tensor
+
+    def run_phonon_analysis(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Perform Phonon calculation, generate force constants, and plot band structure & DOS."""
+        self.log(f"Starting phonon analysis for {self.jid}")
+        phonopy_bands_figname = f"ph_{self.jid}_{self.calculator_type}.png"
+
+        # Phonon generation parameters
+        dim = self.phonon_settings.get('dim', [2, 2, 2])
+        # Define the conversion factor from THz to cm^-1
+        THz_to_cm = 33.35641  # 1 THz = 33.35641 cm^-1
+
+        force_constants_filename = "FORCE_CONSTANTS"
+        eigenvalues_filename = "phonon_eigenvalues.txt"
+        thermal_props_filename = "thermal_properties.txt"
+        write_fc = True
+        min_freq_tol_cm = -5.0  # in cm^-1
+        distance = self.phonon_settings.get('distance', 0.2)
+
+        # Generate k-point path
+        kpoints = Kpoints().kpath(relaxed_atoms, line_density=5)
+
+        # Convert atoms to Phonopy-compatible object
+        self.log("Converting atoms to Phonopy-compatible format...")
+        bulk = relaxed_atoms.phonopy_converter()
+        phonon = Phonopy(
+            bulk,
+            [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]],
+            # Do not set factor here to keep frequencies in THz during calculations
+        )
+
+        # Generate displacements
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+        self.log(f"Generated {len(supercells)} supercells for displacements.")
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for idx, scell in enumerate(supercells):
+            self.log(f"Calculating forces for supercell {idx+1}...")
+            ase_atoms = AseAtoms(
+                symbols=scell.symbols,
+                positions=scell.positions,
+                cell=scell.cell,
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+
+            # Correct for drift force
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+
+            set_of_forces.append(forces)
+
+        # Generate force constants
+        self.log("Producing force constants...")
+        phonon.produce_force_constants(forces=set_of_forces)
+
+        # Write force constants to file if required
+        if write_fc:
+            force_constants_filepath = os.path.join(
+                self.output_dir, force_constants_filename
+            )
+            self.log(f"Writing force constants to {force_constants_filepath}...")
+            write_FORCE_CONSTANTS(
+                phonon.force_constants, filename=force_constants_filepath
+            )
+            self.log(f"Force constants saved to {force_constants_filepath}")
+
+        # Prepare bands for band structure calculation
+        bands = [kpoints.kpts]  # Assuming kpoints.kpts is a list of q-points
+
+        # Prepare labels and path_connections
+        labels = []
+        path_connections = []
+        for i, label in enumerate(kpoints.labels):
+            if label:
+                labels.append(label)
+            else:
+                labels.append("")  # Empty string for points without labels
+
+        # Since we have a single path, set path_connections accordingly
+        path_connections = [True] * (len(bands) - 1)
+        path_connections.append(False)
+
+        # Run band structure calculation with labels
+        self.log("Running band structure calculation...")
+        phonon.run_band_structure(
+            bands,
+            with_eigenvectors=False,
+            labels=labels,
+            path_connections=path_connections,
+        )
+
+        # Write band.yaml file (frequencies will be in THz)
+        band_yaml_filepath = os.path.join(self.output_dir, "band.yaml")
+        self.log(f"Writing band structure data to {band_yaml_filepath}...")
+        phonon.band_structure.write_yaml(filename=band_yaml_filepath)
+        self.log(f"band.yaml saved to {band_yaml_filepath}")
+
+        # --- Begin post-processing to convert frequencies to cm^-1 while preserving formatting ---
+        from ruamel.yaml import YAML
+
+        self.log(f"Converting frequencies in {band_yaml_filepath} to cm^-1 while preserving formatting...")
+        yaml = YAML()
+        yaml.preserve_quotes = True
+
+        with open(band_yaml_filepath, 'r') as f:
+            band_data = yaml.load(f)
+
+        for phonon_point in band_data['phonon']:
+            for band in phonon_point['band']:
+                freq = band['frequency']
+                if freq is not None:
+                    band['frequency'] = freq * THz_to_cm
+
+        with open(band_yaml_filepath, 'w') as f:
+            yaml.dump(band_data, f)
+
+        self.log(f"Frequencies in {band_yaml_filepath} converted to cm^-1 with formatting preserved")
+        # --- End post-processing ---
+
+        # Phonon band structure and eigenvalues
+        lbls = kpoints.labels
+        lbls_ticks = []
+        freqs = []
+        lbls_x = []
+        count = 0
+        eigenvalues = []
+
+        for ii, k in enumerate(kpoints.kpts):
+            k_str = ",".join(map(str, k))
+            if ii == 0 or k_str != ",".join(map(str, kpoints.kpts[ii - 1])):
+                freqs_at_k = phonon.get_frequencies(k)  # Frequencies in THz
+                freqs_at_k_cm = freqs_at_k * THz_to_cm  # Convert to cm^-1
+                freqs.append(freqs_at_k_cm)
+                eigenvalues.append((k, freqs_at_k_cm))  # Store frequencies in cm^-1
+                lbl = "$" + str(lbls[ii]) + "$" if lbls[ii] else ""
+                if lbl:
+                    lbls_ticks.append(lbl)
+                    lbls_x.append(count)
+                count += 1
+
+        # Write eigenvalues to file with frequencies in cm^-1
+        eigenvalues_filepath = os.path.join(self.output_dir, eigenvalues_filename)
+        self.log(f"Writing phonon eigenvalues to {eigenvalues_filepath}...")
+        with open(eigenvalues_filepath, "w") as eig_file:
+            eig_file.write("k-points\tFrequencies (cm^-1)\n")
+            for k, freqs_at_k_cm in eigenvalues:
+                k_str = ",".join(map(str, k))
+                freqs_str = "\t".join(map(str, freqs_at_k_cm))
+                eig_file.write(f"{k_str}\t{freqs_str}\n")
+        self.log(f"Phonon eigenvalues saved to {eigenvalues_filepath}")
+
+        # Convert frequencies to numpy array in cm^-1
+        freqs = np.array(freqs)
+
+        # Plot phonon band structure and DOS
+        the_grid = plt.GridSpec(1, 2, width_ratios=[3, 1], wspace=0.0)
+        plt.rcParams.update({"font.size": 18})
+        plt.figure(figsize=(10, 5))
+
+        # Plot phonon bands
+        plt.subplot(the_grid[0])
+        for i in range(freqs.shape[1]):
+            plt.plot(freqs[:, i], lw=2, c="b")
+        for i in lbls_x:
+            plt.axvline(x=i, c="black")
+        plt.xticks(lbls_x, lbls_ticks)
+        plt.ylabel("Frequency (cm$^{-1}$)")
+        plt.xlim([0, max(lbls_x)])
+
+        # Run mesh and DOS calculations
+        phonon.run_mesh([40, 40, 40], is_gamma_center=True, is_mesh_symmetry=False)
+        phonon.run_total_dos()
+        tdos = phonon.total_dos
+        freqs_dos = np.array(tdos.frequency_points) * THz_to_cm  # Convert to cm^-1
+        dos_values = tdos.dos
+        min_freq = min_freq_tol_cm  # in cm^-1
+        max_freq = max(freqs_dos)
+
+        plt.ylim([min_freq, max_freq])
+
+        # Plot DOS
+        plt.subplot(the_grid[1])
+        plt.fill_between(
+            dos_values, freqs_dos, color=(0.2, 0.4, 0.6, 0.6), edgecolor="k", lw=1, y2=0
+        )
+        plt.xlabel("DOS")
+        plt.yticks([])
+        plt.xticks([])
+        plt.ylim([min_freq, max_freq])
+        plt.xlim([0, max(dos_values)])
+
+        # Save the plot
+        os.makedirs(self.output_dir, exist_ok=True)
+        plot_filepath = os.path.join(self.output_dir, phonopy_bands_figname)
+        plt.tight_layout()
+        plt.savefig(plot_filepath)
+        self.log(
+            f"Phonon band structure and DOS combined plot saved to {plot_filepath}"
+        )
+        plt.close()
+
+        self.log("Calculating thermal properties...")
+        phonon.run_mesh(mesh=[20, 20, 20])
+        phonon.run_thermal_properties(t_step=10, t_max=1000, t_min=0)
+        tprop_dict = phonon.get_thermal_properties_dict()
+
+        # Plot thermal properties
+        plt.figure()
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["free_energy"],
+            label="Free energy (kJ/mol)",
+            color="red",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["entropy"],
+            label="Entropy (J/K*mol)",
+            color="blue",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["heat_capacity"],
+            label="Heat capacity (J/K*mol)",
+            color="green",
+        )
+        plt.legend()
+        plt.xlabel("Temperature (K)")
+        plt.ylabel("Thermal Properties")
+        plt.title("Thermal Properties")
+
+        thermal_props_plot_filepath = os.path.join(
+            self.output_dir, f"Thermal_Properties_{self.jid}.png"
+        )
+        plt.savefig(thermal_props_plot_filepath)
+        self.log(f"Thermal properties plot saved to {thermal_props_plot_filepath}")
+        plt.close()
+
+        # Save thermal properties to file
+        thermal_props_filepath = os.path.join(self.output_dir, thermal_props_filename)
+        with open(thermal_props_filepath, "w") as f:
+            f.write(
+                "Temperature (K)\tFree Energy (kJ/mol)\tEntropy (J/K*mol)\tHeat Capacity (J/K*mol)\n"
+            )
+            for i in range(len(tprop_dict["temperatures"])):
+                f.write(
+                    f"{tprop_dict['temperatures'][i]}\t{tprop_dict['free_energy'][i]}\t"
+                    f"{tprop_dict['entropy'][i]}\t{tprop_dict['heat_capacity'][i]}\n"
+                )
+        self.log(f"Thermal properties written to {thermal_props_filepath}")
+
+        # Calculate zero-point energy (ZPE)
+        zpe = tprop_dict["free_energy"][0] * 0.0103643  # Converting from kJ/mol to eV
+        self.log(f"Zero-point energy: {zpe} eV")
+
+        # Save to job info
+        self.job_info["phonopy_bands"] = phonopy_bands_figname
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return phonon, zpe
+
+    def analyze_defects(self):
+        """Analyze defects by generating, relaxing, and calculating vacancy formation energy."""
+        self.log("Starting defect analysis...")
+        generate_settings = self.defect_settings.get('generate_settings', {})
+        on_conventional_cell = generate_settings.get('on_conventional_cell', True)
+        enforce_c_size = generate_settings.get('enforce_c_size', 8)
+        extend = generate_settings.get('extend', 1)
+    # Generate defect structures from the original atoms
+        defect_structures = Vacancy(self.atoms).generate_defects(on_conventional_cell=on_conventional_cell, enforce_c_size=enforce_c_size, extend=extend)
+
+        for defect in defect_structures:
+        # Extract the defect structure and related metadata
+            defect_structure = Atoms.from_dict(defect.to_dict()["defect_structure"])
+        
+        # Construct a consistent defect name without Wyckoff notation
+            element = defect.to_dict()['symbol']
+            defect_name = f"{self.jid}_{element}"  # Consistent format
+            self.log(f"Analyzing defect: {defect_name}")
+
+        # Relax the defect structure
+            relaxed_defect_atoms = self.relax_defect_structure(defect_structure, name=defect_name)
+
+            if relaxed_defect_atoms is None:
+                self.log(f"Skipping {defect_name} due to failed relaxation.")
+                continue
+
+        # Retrieve energies for calculating the vacancy formation energy
+            vacancy_energy = self.job_info.get(f"final_energy_defect for {defect_name}")
+            bulk_energy = (
+                self.job_info.get("equilibrium_energy")
+                / self.atoms.num_atoms
+                * (defect_structure.num_atoms + 1)
+            )
+
+            if vacancy_energy is None or bulk_energy is None:
+                self.log(f"Skipping {defect_name} due to missing energy values.")
+                continue
+
+        # Get chemical potential and calculate vacancy formation energy
+            chemical_potential = self.get_chemical_potential(element)
+
+            if chemical_potential is None:
+                self.log(f"Skipping {defect_name} due to missing chemical potential for {element}.")
+                continue
+
+            vacancy_formation_energy = vacancy_energy - bulk_energy + chemical_potential
+
+        # Log and store the vacancy formation energy consistently
+            self.job_info[f"vacancy_formation_energy for {defect_name}"] = vacancy_formation_energy
+            self.log(f"Vacancy formation energy for {defect_name}: {vacancy_formation_energy} eV")
+
+    # Save the job info to a JSON file
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+        self.log("Defect analysis completed.")
+
+    def relax_defect_structure(self, atoms, name):
+        """Relax the defect structure and log the process."""
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.defect_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.defect_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log the final energy and relaxation status
+        self.log(
+            f"Final energy of FIRE optimization for defect structure: {final_energy}"
+        )
+        self.log(
+            f"Defect relaxation {'converged' if converged else 'did not converge'} within 200 steps."
+        )
+
+        # Update job info with the final energy and convergence status
+        self.job_info[f"final_energy_defect for {name}"] = final_energy
+        self.job_info[f"converged for {name}"] = converged
+
+        if converged:
+            poscar_filename = os.path.join(self.output_dir, f"POSCAR_{name}_relaxed.vasp")
+            poscar_defect = Poscar(relaxed_atoms)
+            poscar_defect.write_file(poscar_filename)
+            self.log(f"Relaxed defect structure saved to {poscar_filename}")
+
+        return relaxed_atoms if converged else None
+
+    def analyze_surfaces(self):
+        """
+        Perform surface analysis by generating surface structures, relaxing them, and calculating surface energies.
+        """
+        self.log(f"Analyzing surfaces for {self.jid}")
+
+        indices_list = self.surface_settings.get('indices_list', [
+            [1, 0, 0],
+            [1, 1, 1],
+            [1, 1, 0],
+            [0, 1, 1],
+            [0, 0, 1],
+            [0, 1, 0],
+        ])
+        layers = self.surface_settings.get('layers', 4)
+        vacuum = self.surface_settings.get('vacuum', 18)
+
+        for indices in indices_list:
+            # Generate surface and check for polarity
+            surface = (
+                Surface(atoms=self.atoms, indices=indices, layers=layers, vacuum=vacuum)
+                .make_surface()
+                .center_around_origin()
+            )
+            if surface.check_polar:
+                self.log(
+                    f"Skipping polar surface for {self.jid} with indices {indices}"
+                )
+                continue
+
+            # Write initial POSCAR for surface
+            poscar_surface = Poscar(atoms=surface)
+            poscar_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}.vasp",
+                )
+            )
+
+            # Relax the surface structure
+            relaxed_surface_atoms, final_energy = self.relax_surface_structure(
+                surface, indices
+            )
+
+            # If relaxation failed, skip further calculations
+            if relaxed_surface_atoms is None:
+                self.log(f"Skipping surface {indices} due to failed relaxation.")
+                continue
+
+            # Write relaxed POSCAR for surface
+            pos_relaxed_surface = Poscar(relaxed_surface_atoms)
+            pos_relaxed_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}_relaxed.vasp",
+                )
+            )
+
+            # Calculate and log surface energy
+            bulk_energy = self.job_info.get("equilibrium_energy")
+            if final_energy is None or bulk_energy is None:
+                self.log(
+                    f"Skipping surface energy calculation for {self.jid} with indices {indices} due to missing energy values."
+                )
+                continue
+
+            surface_energy = self.calculate_surface_energy(
+                final_energy, bulk_energy, relaxed_surface_atoms, surface
+            )
+
+            # Store the surface energy with the new naming convention
+            surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+            self.job_info[surface_name] = surface_energy
+            self.log(
+                f"Surface energy for {self.jid} with indices {indices}: {surface_energy} J/m^2"
+            )
+
+        # Save updated job info
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log("Surface analysis completed.")
+
+    def relax_surface_structure(self, atoms, indices):
+        """
+        Relax the surface structure and log the process.
+        """
+        filter_type = self.surface_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.surface_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        self.log(f"Starting surface relaxation for {self.jid} with indices {indices}")
+        start_time = time.time()
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+        # Implement other filters if needed
+            pass
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log relaxation results
+        self.log(
+            f"Final energy of FIRE optimization for surface structure: {final_energy}"
+        )
+        self.log(
+            f"Surface relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        end_time = time.time()
+        self.log(
+            f"Surface Relaxation Calculation time: {end_time - start_time} seconds"
+        )
+
+        # Update job info and return relaxed atoms if converged, otherwise return None
+        self.job_info[f"final_energy_surface_{indices}"] = final_energy
+        self.job_info[f"converged_surface_{indices}"] = converged
+
+        # Return both relaxed atoms and the final energy as a tuple
+        return (relaxed_atoms if converged else None), final_energy
+
+    def calculate_surface_energy(
+        self, final_energy, bulk_energy, relaxed_atoms, surface
+    ):
+        """
+        Calculate the surface energy based on the final energy of the relaxed surface and bulk energy.
+        """
+        # Calculate the number of bulk units in the surface supercell
+        num_units = surface.num_atoms / self.atoms.num_atoms
+
+        # Calculate the surface area using the lattice vectors
+        m = relaxed_atoms.lattice.matrix
+        surface_area = np.linalg.norm(np.cross(m[0], m[1]))
+
+        # Calculate surface energy in J/m^2
+        surface_energy = (
+            (final_energy - bulk_energy * num_units) * 16.02176565 / (2 * surface_area)
+        )
+
+        return surface_energy
+
+    def run_phonon3_analysis(self, relaxed_atoms):
+        from phono3py import Phono3py
+        """Run Phono3py analysis, process results, and generate thermal conductivity data."""
+        self.log(f"Starting Phono3py analysis for {self.jid}")
+
+        # Set parameters for the Phono3py calculation
+        dim = self.phonon3_settings.get('dim', [2, 2, 2])
+        distance = self.phonon3_settings.get('distance', 0.2)
+
+        #force_multiplier = 16
+
+        # Convert atoms to Phonopy-compatible object and set up Phono3py
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        bulk = relaxed_atoms.phonopy_converter()
+
+        phonon = Phono3py(bulk, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]])
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for scell in supercells:
+            ase_atoms = AseAtoms(
+                symbols=scell.get_chemical_symbols(),
+                scaled_positions=scell.get_scaled_positions(),
+                cell=scell.get_cell(),
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+            set_of_forces.append(forces)
+
+        # Set the forces and produce third-order force constants
+        forces = np.array(set_of_forces).reshape(-1, len(phonon.supercell), 3)
+        phonon.forces = forces
+        phonon.produce_fc3()
+
+        # Run thermal conductivity calculation
+        phonon.mesh_numbers = 30
+        phonon.init_phph_interaction()
+        phonon.run_thermal_conductivity(
+            temperatures=range(0, 1001, 10), write_kappa=True
+        )
+
+        # Collect thermal conductivity data
+        kappa = phonon.thermal_conductivity.kappa
+        self.log(f"Thermal conductivity: {kappa}")
+
+        # Move generated HDF5 files to the output directory
+        hdf5_file_pattern = "kappa-*.hdf5"
+        for hdf5_file in glob.glob(hdf5_file_pattern):
+            shutil.move(hdf5_file, os.path.join(self.output_dir, hdf5_file))
+
+        # Process Phono3py results and save plots
+        self.process_phonon3_results()
+
+        # Save updated job info to JSON
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(f"Phono3py analysis completed for {self.jid}")
+
+    def process_phonon3_results(self):
+        """Process Phono3py results and generate plots of thermal conductivity."""
+        file_pattern = os.path.join(self.output_dir, "kappa-*.hdf5")
+        file_list = glob.glob(file_pattern)
+
+        temperatures = np.arange(10, 101, 10)
+        kappa_xx_values = []
+
+        if file_list:
+            hdf5_filename = file_list[0]
+            self.log(f"Processing file: {hdf5_filename}")
+
+            for temperature_index in temperatures:
+                converted_kappa = self.convert_kappa_units(
+                    hdf5_filename, temperature_index
+                )
+                kappa_xx = converted_kappa[0]
+                kappa_xx_values.append(kappa_xx)
+                self.log(
+                    f"Temperature index {temperature_index}, converted kappa: {kappa_xx}"
+                )
+
+            # Save results to job_info
+            self.job_info["temperatures"] = temperatures.tolist()
+            self.job_info["kappa_xx_values"] = kappa_xx_values
+
+            # Plot temperature vs. converted kappa (xx element)
+            plt.figure(figsize=(8, 6))
+            plt.plot(
+                temperatures * 10, kappa_xx_values, marker="o", linestyle="-", color="b"
+            )
+            plt.xlabel("Temperature (K)")
+            plt.ylabel("Converted Kappa (xx element)")
+            plt.title("Temperature vs. Converted Kappa (xx element)")
+            plt.grid(True)
+            plt.savefig(
+                os.path.join(self.output_dir, "Temperature_vs_Converted_Kappa.png")
+            )
+            plt.close()
+        else:
+            self.log("No files matching the pattern were found.")
+
+    def convert_kappa_units(self, hdf5_filename, temperature_index):
+        """Convert thermal conductivity kappa from HDF5 file units."""
+        with h5py.File(hdf5_filename, "r") as f:
+            kappa_unit_conversion = f["kappa_unit_conversion"][()]
+            heat_capacity = f["heat_capacity"][:]
+            gv_by_gv = f["gv_by_gv"][:]
+            gamma = f["gamma"][:]
+
+            converted_kappa = (
+                kappa_unit_conversion
+                * heat_capacity[temperature_index, 2, 0]
+                * gv_by_gv[2, 0]
+                / (2 * gamma[temperature_index, 2, 0])
+            )
+
+            return converted_kappa
+
+    def calculate_thermal_expansion(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Calculate the thermal expansion coefficient using QHA."""
+
+        def log(message):
+            with open(self.log_file, "a") as f:
+                f.write(message + "\n")
+            print(message)
+
+        log("Starting thermal expansion analysis...")
+
+        # Step 1: Calculate finer E-V curve
+        vol, y, strained_structures, eos, kv, e0, v0 = self.fine_ev_curve(
+            atoms=relaxed_atoms, dx=np.linspace(-0.05, 0.05, 50)  # Denser grid
+        )
+
+        # Log Bulk modulus, equilibrium energy, and volume
+        log(
+            f"Bulk modulus: {kv} GPa, Equilibrium energy: {y[0]} eV, Volume: {vol[0]} ų"
+        )
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = y[0]
+        self.job_info["equilibrium_volume"] = vol[0]
+
+        # Step 2: Generate phonons for strained structures
+        free_energies, heat_capacities, entropies, temperatures = (
+            self.generate_phonons_for_volumes(
+                strained_structures,
+                calculator=self.calculator,
+                dim=[2, 2, 2],
+                distance=0.2,
+                mesh=[20, 20, 20],
+            )
+        )
+
+        # Step 3: Perform QHA-based thermal expansion analysis
+        alpha = self.perform_qha(
+            volumes=vol,
+            energies=y,
+            free_energies=free_energies,
+            heat_capacities=heat_capacities,
+            entropies=entropies,
+            temperatures=temperatures,
+            output_dir=self.output_dir,
+        )
+
+        self.log(f"Thermal expansion coefficient calculated: {alpha}")
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(
+            f"Thermal expansion analysis information saved to file: {self.jid}_{self.calculator_type}_job_info.json"
+        )
+
+    # Helper Functions Inside the Class
+    def fine_ev_curve(self, atoms, dx=np.linspace(-0.05, 0.05, 50)):
+        """
+        Generate a finer energy-volume curve for strained structures.
+        """
+        y = []
+        vol = []
+        strained_structures = []
+
+        for i in dx:
+            # Apply strain and get strained atoms
+            strained_atoms = atoms.strain_atoms(i)
+            ase_atoms = strained_atoms.ase_converter()  # Convert to ASE Atoms
+            ase_atoms.calc = self.calculator  # Assign the calculator
+
+            # Get potential energy and volume
+            energy = ase_atoms.get_potential_energy()
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+            strained_structures.append(strained_atoms)  # Save the strained structure
+
+        vol = np.array(vol)
+        y = np.array(y)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        eos = EquationOfState(vol, y, eos="murnaghan")
+        v0, e0, B = eos.fit()
+        kv = B / kJ * 1.0e24  # Convert to GPa
+
+        # Log important results
+        self.log(f"Bulk modulus: {kv} GPa")
+        self.log(f"Equilibrium energy: {e0} eV")
+        self.log(f"Equilibrium volume: {v0} ų")
+
+        # Save E-V curve plot
+        fig = plt.figure()
+        eos.plot()
+        ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+        fig.savefig(ev_plot_filename)
+        plt.close(fig)
+        self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+        # Save E-V curve data to a text file
+        ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+        with open(ev_data_filename, "w") as f:
+            f.write("Volume (ų)\tEnergy (eV)\n")
+            for v, e in zip(vol, y):
+                f.write(f"{v}\t{e}\n")
+        self.log(f"E-V curve data saved to {ev_data_filename}")
+
+        # Update job info with the results
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = e0
+        self.job_info["equilibrium_volume"] = v0
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+    def generate_phonons_for_volumes(
+        self, structures, calculator, dim=[2, 2, 2], distance=0.2, mesh=[20, 20, 20]
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        all_free_energies = []
+        all_heat_capacities = []
+        all_entropies = []
+        temperatures = np.arange(0, 300, 6)  # Define temperature range
+
+        for structure in structures:
+            # Convert structure to PhonopyAtoms
+            phonopy_atoms = PhonopyAtoms(
+                symbols=[str(e) for e in structure.elements],
+                positions=structure.cart_coords,
+                cell=structure.lattice.matrix,
+            )
+
+            # Initialize Phonopy object
+            phonon = Phonopy(
+                phonopy_atoms, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]]
+            )
+            phonon.generate_displacements(distance=distance)
+
+            # Calculate forces for displaced structures
+            supercells = phonon.get_supercells_with_displacements()
+            forces = []
+            for scell in supercells:
+                ase_atoms = AseAtoms(
+                    symbols=scell.symbols,
+                    positions=scell.positions,
+                    cell=scell.cell,
+                    pbc=True,
+                )
+                ase_atoms.calc = calculator
+                forces.append(ase_atoms.get_forces())
+
+            phonon.produce_force_constants(forces=forces)
+
+            # Post-processing to get thermal properties
+            phonon.run_mesh(mesh=mesh)
+            phonon.run_thermal_properties(t_min=0, t_step=6, t_max=294)
+            tprop_dict = phonon.get_thermal_properties_dict()
+
+            free_energies = tprop_dict["free_energy"]
+            heat_capacities = tprop_dict["heat_capacity"]
+            entropies = tprop_dict["entropy"]
+
+            all_entropies.append(entropies)
+            all_free_energies.append(free_energies)
+            all_heat_capacities.append(heat_capacities)
+
+        return (
+            np.array(all_free_energies),
+            np.array(all_heat_capacities),
+            np.array(all_entropies),
+            temperatures,
+        )
+
+    def perform_qha(
+        self,
+        volumes,
+        energies,
+        free_energies,
+        heat_capacities,
+        entropies,
+        temperatures,
+        output_dir,
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        # Debugging: print array sizes
+        print(f"Number of temperatures: {len(temperatures)}")
+        print(f"Number of free energy data points: {free_energies.shape}")
+        print(f"Number of volume data points: {len(volumes)}")
+
+        # Ensure that volumes, free_energies, and temperatures are consistent
+        if len(volumes) != len(temperatures):
+            raise ValueError(
+                "The number of volumes must match the number of temperatures"
+            )
+
+        # Initialize the QHA object
+        try:
+            qha = PhonopyQHA(
+                volumes=volumes,
+                electronic_energies=energies,
+                free_energy=free_energies,
+                cv=heat_capacities,
+                entropy=entropies,
+                temperatures=temperatures,
+                eos="murnaghan",  # or another EOS if needed
+                verbose=True,
+            )
+        except IndexError as e:
+            print(f"Error in QHA initialization: {e}")
+            raise
+
+        # Calculate thermal expansion and save plots
+        thermal_expansion_plot = os.path.join(output_dir, "thermal_expansion.png")
+        volume_temperature_plot = os.path.join(output_dir, "volume_temperature.png")
+        helmholtz_volume_plot = os.path.join(output_dir, "helmholtz_volume.png")
+
+        qha.get_thermal_expansion()
+
+        # Save thermal expansion plot
+        qha.plot_thermal_expansion()
+        plt.savefig(thermal_expansion_plot)
+
+        # Save volume-temperature plot
+        qha.plot_volume_temperature()
+        plt.savefig(volume_temperature_plot)
+
+        # Save Helmholtz free energy vs. volume plot
+        qha.plot_helmholtz_volume()
+        plt.savefig(helmholtz_volume_plot)
+
+        # Optionally save thermal expansion coefficient to a file
+        thermal_expansion_file = os.path.join(output_dir, "thermal_expansion.txt")
+        alpha = qha.write_thermal_expansion(filename=thermal_expansion_file)
+
+        return alpha
+
+    def general_melter(self, relaxed_atoms):
+        """Perform MD simulation to melt the structure, then quench it back to room temperature."""
+        self.log(f"Starting MD melting and quenching simulation for {self.jid}")
+
+        calculator = self.setup_calculator()
+        ase_atoms = relaxed_atoms.ase_converter()
+        dim = self.ensure_cell_size(ase_atoms, min_size=self.md_settings.get('min_size', 10.0))
+        supercell = relaxed_atoms.make_supercell_matrix(dim)
+        ase_atoms = supercell.ase_converter()
+        ase_atoms.calc = calculator
+
+        dt = self.md_settings.get('dt', 1) * ase.units.fs
+        temp0 = self.md_settings.get('temp0', 3500)
+        nsteps0 = self.md_settings.get('nsteps0', 1000)
+        temp1 = self.md_settings.get('temp1', 300)
+        nsteps1 = self.md_settings.get('nsteps1', 2000)
+        taut = self.md_settings.get('taut', 20) * ase.units.fs
+        trj = os.path.join(self.output_dir, f"{self.jid}_melt.traj")
+
+        # Initialize velocities and run the first part of the MD simulation
+        from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+        from ase.md.nvtberendsen import NVTBerendsen
+
+        MaxwellBoltzmannDistribution(ase_atoms, temp0 * ase.units.kB)
+        dyn = NVTBerendsen(ase_atoms, dt, temp0, taut=taut, trajectory=trj)
+
+        def myprint():
+            message = f"time={dyn.get_time() / ase.units.fs: 5.0f} fs T={ase_atoms.get_temperature(): 3.0f} K"
+            self.log(message)
+
+        dyn.attach(myprint, interval=20)
+        dyn.run(nsteps0)
+
+        # Cool down to room temperature
+        dyn.set_temperature(temp1)
+        dyn.run(nsteps1)
+
+        # Convert back to JARVIS atoms and save the final structure
+        final_atoms = ase_to_atoms(ase_atoms)
+        poscar_filename = os.path.join(
+            self.output_dir, f"POSCAR_{self.jid}_quenched_{self.calculator_type}.vasp"
+        )
+        from ase.io import write
+
+        write(poscar_filename, final_atoms.ase_converter(), format="vasp")
+        self.log(f"MD simulation completed. Final structure saved to {poscar_filename}")
+        self.job_info["quenched_atoms"] = final_atoms.to_dict()
+
+        return final_atoms
+
+    def calculate_rdf(self, quenched_atoms):
+        """Calculate Radial Distribution Function (RDF) for quenched structure and save plot."""
+        self.log(f"Starting RDF calculation for {self.jid}")
+        ase_atoms = quenched_atoms.ase_converter()
+        rmax = 3.5
+        nbins = 200
+
+        def perform_rdf_calculation(rmax):
+            from ase.ga.utilities import get_rdf
+
+            rdfs, distances = get_rdf(ase_atoms, rmax, nbins)
+            plt.figure()
+            plt.plot(distances, rdfs)
+            plt.xlabel("Distance (Å)")
+            plt.ylabel("RDF")
+            plt.title(
+                f"Radial Distribution Function for {self.jid} ({self.calculator_type})"
+            )
+            rdf_plot_filename = os.path.join(
+                self.output_dir, f"RDF_{self.jid}_{self.calculator_type}.png"
+            )
+            plt.savefig(rdf_plot_filename)
+            plt.close()
+            self.job_info["rdf_plot"] = rdf_plot_filename
+            self.log(f"RDF plot saved to {rdf_plot_filename}")
+            return rdf_plot_filename
+
+        try:
+            perform_rdf_calculation(rmax)
+        except ValueError as e:
+            if "The cell is not large enough" in str(e):
+                recommended_rmax = float(str(e).split("<")[1].split("=")[1])
+                self.log(f"Error: {e}. Adjusting rmax to {recommended_rmax}.")
+                perform_rdf_calculation(recommended_rmax)
+            else:
+                self.log(f"Error: {e}")
+                raise
+
+    def ensure_cell_size(self, ase_atoms, min_size):
+        """Ensure that all cell dimensions are at least min_size."""
+        cell_lengths = ase_atoms.get_cell().lengths()
+        scale_factors = np.ceil(min_size / cell_lengths).astype(int)
+        supercell_dims = [max(1, scale) for scale in scale_factors]
+        return supercell_dims
+
+    def analyze_interfaces(self):
+        """Perform interface analysis using intermat package."""
+        if not self.film_jid or not self.substrate_jid:
+            self.log("Film JID or substrate JID not provided, skipping interface analysis.")
+            return
+
+        self.log(f"Starting interface analysis between {self.film_jid} and {self.substrate_jid}")
+
+        # Ensure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # Prepare config
+        config = {
+            "film_jid": self.film_jid,
+            "substrate_jid": self.substrate_jid,
+            "film_index": self.film_index,
+            "substrate_index": self.substrate_index,
+            "disp_intvl": 0.05,
+            "calculator_method": self.calculator_type.lower(),
+        }
+
+        config_filename = os.path.join(
+            self.output_dir,
+            f"config_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.json"
+        )
+
+        # Save config file
+        save_dict_to_json(config, config_filename)
+        self.log(f"Config file created: {config_filename}")
+
+        # Run intermat script using subprocess in self.output_dir
+        command = f"run_intermat.py --config_file {os.path.basename(config_filename)}"
+        self.log(f"Running command: {command}")
+
+        try:
+            result = subprocess.run(
+                command,
+                shell=True,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=self.output_dir  # Set the working directory for the subprocess
+            )
+            self.log(f"Command output: {result.stdout}")
+        except subprocess.CalledProcessError as e:
+            self.log(f"Command failed with error: {e.stderr}")
+            return
+
+        # After execution, check for outputs in self.output_dir
+        main_results_filename = os.path.join(self.output_dir, "intermat_results.json")
+        if not os.path.exists(main_results_filename):
+            self.log(f"Results file not found: {main_results_filename}")
+            return
+
+        res = load_dict_from_json(main_results_filename)
+        w_adhesion = res.get("wads", [])
+        systems_info = res.get("systems", {})
+
+        # Handle intmat.png
+        intmat_filename = os.path.join(self.output_dir, "intmat.png")
+        if os.path.exists(intmat_filename):
+            new_intmat_filename = os.path.join(
+                self.output_dir,
+                f"intmat_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.png"
+            )
+            os.rename(intmat_filename, new_intmat_filename)
+            self.job_info["intmat_plot"] = new_intmat_filename
+            self.log(f"intmat.png saved as {new_intmat_filename}")
+        else:
+            self.log("intmat.png not found.")
+
+        if "wads" in res:
+            # Save additional plots or data as needed
+            self.job_info["interface_scan_results"] = main_results_filename
+            self.job_info["w_adhesion"] = w_adhesion
+            self.job_info["systems_info"] = systems_info
+            self.log(f"Interface scan results saved to {main_results_filename}")
+            self.log(f"w_adhesion: {w_adhesion}")
+            self.log(f"systems_info: {systems_info}")
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+        else:
+            self.log(f"No 'wads' key in results file: {main_results_filename}")
+
+    def get_job_info_filename(self):
+        if hasattr(self, 'jid') and self.jid:
+            return os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            )
+        else:
+            return os.path.join(
+                self.output_dir, f"Interface_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}_job_info.json"
+            )
+
+    import numpy as np
+    import pandas as pd
+    from sklearn.metrics import mean_absolute_error, r2_score
+    import matplotlib.pyplot as plt
+    from matplotlib.gridspec import GridSpec
+    import os
+
+    def run_all(self):
+        """Run selected analyses based on configuration."""
+        # Start timing the entire run
+        start_time = time.time()
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for analysis.")
+            self.atoms = self.atoms.get_conventional_atoms
+        else:
+            self.atoms = self.atoms
+        # Relax the structure if specified
+        if 'relax_structure' in self.properties_to_calculate:
+            relaxed_atoms = self.relax_structure()
+        else:
+            relaxed_atoms = self.atoms
+
+        # Proceed only if the structure is relaxed or original atoms are used
+        if relaxed_atoms is None:
+            self.log("Relaxation did not converge. Exiting.")
+            return
+
+        if 'calculate_forces' in self.properties_to_calculate:
+            self.calculate_forces(self.atoms)
+
+        # Lattice parameters before and after relaxation
+        lattice_initial = self.atoms.lattice
+        lattice_final = relaxed_atoms.lattice
+
+        # Prepare final results dictionary
+        final_results = {}
+
+        # Initialize variables for error calculation
+        err_a = err_b = err_c = err_vol = err_form = err_kv = err_c11 = err_c44 = err_surf_en = err_vac_en = np.nan
+        form_en_entry = kv_entry = c11_entry = c44_entry = 0
+
+        # Calculate E-V curve and bulk modulus if specified
+        if 'calculate_ev_curve' in self.properties_to_calculate:
+            _, _, _, _, bulk_modulus, _, _ = self.calculate_ev_curve(relaxed_atoms)
+            kv_entry = self.reference_data.get("bulk_modulus_kv", 0)
+            final_results['modulus'] = {
+                'kv': bulk_modulus,
+                'kv_entry': kv_entry
+            }
+            err_kv = mean_absolute_error([kv_entry], [bulk_modulus]) if bulk_modulus is not None else np.nan
+
+                
+        # Formation energy
+        if 'calculate_formation_energy' in self.properties_to_calculate:
+            formation_energy = self.calculate_formation_energy(relaxed_atoms)
+            form_en_entry = self.reference_data.get("formation_energy_peratom", 0)
+            final_results['form_en'] = {
+                'form_energy': formation_energy,
+                'form_energy_entry': form_en_entry
+            }
+            err_form = mean_absolute_error([form_en_entry], [formation_energy])
+
+        # Elastic tensor
+        if 'calculate_elastic_tensor' in self.properties_to_calculate:
+            elastic_tensor = self.calculate_elastic_tensor(relaxed_atoms)
+            c11_entry = self.reference_data.get("elastic_tensor", [[0]])[0][0]
+            c44_entry = self.reference_data.get("elastic_tensor", [[0, 0, 0, [0, 0, 0, 0]]])[3][3]
+            final_results['elastic_tensor'] = {
+                'c11': elastic_tensor.get("C_11", 0),
+                'c44': elastic_tensor.get("C_44", 0),
+                'c11_entry': c11_entry,
+                'c44_entry': c44_entry
+            }
+            err_c11 = mean_absolute_error([c11_entry], [elastic_tensor.get("C_11", np.nan)])
+            err_c44 = mean_absolute_error([c44_entry], [elastic_tensor.get("C_44", np.nan)])
+
+        # Phonon analysis
+        if 'run_phonon_analysis' in self.properties_to_calculate:
+            phonon, zpe = self.run_phonon_analysis(relaxed_atoms)
+            final_results['zpe'] = zpe
+        else:
+            zpe = None
+
+        # Surface energy analysis
+        if 'analyze_surfaces' in self.properties_to_calculate:
+            self.analyze_surfaces()
+            surf_en, surf_en_entry = [], []
+            surface_entries = get_surface_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+
+            indices_list = self.surface_settings.get('indices_list', [
+                [1, 0, 0],
+                [1, 1, 1],
+                [1, 1, 0],
+                [0, 1, 1],
+                [0, 0, 1],
+                [0, 1, 0],
+            ])
+
+            for indices in indices_list:
+                surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+                calculated_surface_energy = self.job_info.get(surface_name, 0)
+                try:
+                    # Try to match the surface entry
+                    matching_entry = next(
+                        (entry for entry in surface_entries if entry['name'].strip() == surface_name.strip()),
+                        None
+                    )
+                    if matching_entry and calculated_surface_energy != 0 and matching_entry["surf_en_entry"] != 0:
+                        surf_en.append(calculated_surface_energy)
+                        surf_en_entry.append(matching_entry["surf_en_entry"])
+                    else:
+                        print(f"No valid matching entry found for {surface_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing surface {surface_name}: {e}")
+                    self.log(f"Error processing surface {surface_name}: {str(e)}")
+                    continue  # Skip this surface and move to the next one
+            final_results['surface_energy'] = [
+                {
+                    "name": f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}",
+                    "surf_en": se,
+                    "surf_en_entry": see
+                }
+                for se, see, indices in zip(surf_en, surf_en_entry, indices_list)
+            ]
+            err_surf_en = mean_absolute_error(surf_en_entry, surf_en) if surf_en else np.nan
+
+        # Vacancy energy analysis
+        if 'analyze_defects' in self.properties_to_calculate:
+            self.analyze_defects()
+            vac_en, vac_en_entry = [], []
+            vacancy_entries = get_vacancy_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+            for defect in Vacancy(self.atoms).generate_defects(on_conventional_cell=True, enforce_c_size=8, extend=1):
+                defect_name = f"{self.jid}_{defect.to_dict()['symbol']}"
+                vacancy_energy = self.job_info.get(f"vacancy_formation_energy for {defect_name}", 0)
+                try:
+                    # Try to match the vacancy entry
+                    matching_entry = next(
+                        (entry for entry in vacancy_entries if entry['symbol'] == defect_name),
+                        None
+                    )
+                    if matching_entry and vacancy_energy != 0 and matching_entry['vac_en_entry'] != 0:
+                        vac_en.append(vacancy_energy)
+                        vac_en_entry.append(matching_entry['vac_en_entry'])
+                    else:
+                        print(f"No valid matching entry found for {defect_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing defect {defect_name}: {e}")
+                    self.log(f"Error processing defect {defect_name}: {str(e)}")
+                    continue  # Skip this defect and move to the next one
+            final_results['vacancy_energy'] = [
+                {"name": ve_name, "vac_en": ve, "vac_en_entry": vee}
+                for ve_name, ve, vee in zip(
+                    [f"{self.jid}_{defect.to_dict()['symbol']}" for defect in Vacancy(self.atoms).generate_defects(
+                        on_conventional_cell=True, enforce_c_size=8, extend=1
+                    )],
+                    vac_en,
+                    vac_en_entry
+                )
+            ]
+            err_vac_en = mean_absolute_error(vac_en_entry, vac_en) if vac_en else np.nan
+
+        # Additional analyses
+        if 'analyze_interfaces' in self.properties_to_calculate and self.film_jid and self.substrate_jid:
+            self.analyze_interfaces()
+
+        if 'run_phonon3_analysis' in self.properties_to_calculate:
+            self.run_phonon3_analysis(relaxed_atoms)
+
+        if 'calculate_thermal_expansion' in self.properties_to_calculate:
+            self.calculate_thermal_expansion(relaxed_atoms)
+
+        if 'general_melter' in self.properties_to_calculate:
+            quenched_atoms = self.general_melter(relaxed_atoms)
+            if 'calculate_rdf' in self.properties_to_calculate:
+                self.calculate_rdf(quenched_atoms)
+
+        # Record lattice parameters
+        final_results['energy'] = {
+            "initial_a": lattice_initial.a,
+            "initial_b": lattice_initial.b,
+            "initial_c": lattice_initial.c,
+            "initial_vol": lattice_initial.volume,
+            "final_a": lattice_final.a,
+            "final_b": lattice_final.b,
+            "final_c": lattice_final.c,
+            "final_vol": lattice_final.volume,
+            "energy": self.job_info.get("final_energy_structure", 0)
+        }
+
+        # Error calculations
+        err_a = mean_absolute_error([lattice_initial.a], [lattice_final.a])
+        err_b = mean_absolute_error([lattice_initial.b], [lattice_final.b])
+        err_c = mean_absolute_error([lattice_initial.c], [lattice_final.c])
+        err_vol = mean_absolute_error([lattice_initial.volume], [lattice_final.volume])
+
+        # Create an error dictionary
+        error_dat = {
+            "err_a": err_a,
+            "err_b": err_b,
+            "err_c": err_c,
+            "err_form": err_form,
+            "err_vol": err_vol,
+            "err_kv": err_kv,
+            "err_c11": err_c11,
+            "err_c44": err_c44,
+            "err_surf_en": err_surf_en,
+            "err_vac_en": err_vac_en,
+            "time": time.time() - start_time
+        }
+
+        print("Error metrics calculated:", error_dat)
+
+        # Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+        # Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+        # Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        # Write results to a JSON file
+        output_file = os.path.join(self.output_dir, f"{self.jid}_{self.calculator_type}_results.json")
+        save_dict_to_json(final_results, output_file)
+
+        # Log total time
+        total_time = error_dat['time']
+        self.log(f"Total time for run: {total_time} seconds")
+
+        return error_dat
+
+
+# Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+# Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+# Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        return error_dat
+
+    def plot_error_scorecard(self, df):
+        import plotly.express as px
+
+        fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+        unique_dir = os.path.basename(self.output_dir)
+        fname_plot = os.path.join(self.output_dir, f"{unique_dir}_error_scorecard.png")
+        fig.write_image(fname_plot)
+        fig.show()
+
+def analyze_multiple_structures(jid_list, calculator_types, chemical_potentials_file):
+    composite_error_data = {}
+
+    for calculator_type in calculator_types:
+        # List to store individual error DataFrames
+        error_dfs = []
+
+        for jid in jid_list:
+            print(f"Analyzing {jid} with {calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                jid=jid,
+                calculator_type=calculator_type,
+                chemical_potentials_file=chemical_potentials_file,
+            )
+            # Run analysis and get error data
+            error_dat = analyzer.run_all()
+            error_df = pd.DataFrame([error_dat])
+            error_dfs.append(error_df)
+
+        # Concatenate all error DataFrames
+        all_errors_df = pd.concat(error_dfs, ignore_index=True)
+
+        # Compute composite errors by ignoring NaN values
+        composite_error = all_errors_df.mean(skipna=True).to_dict()
+
+        # Store the composite error data for this calculator type
+        composite_error_data[calculator_type] = composite_error
+
+    # Once all materials and calculators have been processed, create a DataFrame
+    composite_df = pd.DataFrame(composite_error_data).transpose()
+
+    # Plot the composite scorecard
+    plot_composite_scorecard(composite_df)
+
+    # Save the composite dataframe
+    composite_df.to_csv("composite_error_data.csv", index=True)
+
+def analyze_multiple_interfaces(film_jid_list, substrate_jid_list, calculator_types, chemical_potentials_file, film_index="1_1_0", substrate_index="1_1_0"):
+    for calculator_type in calculator_types:
+        for film_jid in film_jid_list:
+            for substrate_jid in substrate_jid_list:
+                print(f"Analyzing interface between {film_jid} and {substrate_jid} with {calculator_type}...")
+                analyzer = MaterialsAnalyzer(
+                    calculator_type=calculator_type,
+                    chemical_potentials_file=chemical_potentials_file,
+                    film_jid=film_jid,
+                    substrate_jid=substrate_jid,
+                    film_index=film_index,
+                    substrate_index=substrate_index,
+                )
+                analyzer.analyze_interfaces()
+
+def plot_composite_scorecard(df):
+    """Plot the composite scorecard for all calculators"""
+    fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+    fig.update_layout(title="Composite Scorecard for Calculators")
+    
+    # Save plot
+    fname_plot = "composite_error_scorecard.png"
+    fig.write_image(fname_plot)
+    fig.show()
+
+
+class MLearnForcesAnalyzer:
+    def __init__(self, calculator_type, mlearn_elements, output_dir=None):
+        self.calculator_type = calculator_type
+        self.mlearn_elements = mlearn_elements
+        elements_str = "_".join(self.mlearn_elements)
+        self.output_dir = output_dir or f"mlearn_analysis_{elements_str}_{calculator_type}"
+        os.makedirs(self.output_dir, exist_ok=True)
+        self.log_file = os.path.join(self.output_dir, "mlearn_analysis_log.txt")
+        self.setup_logger()
+        self.calculator = self.setup_calculator()
+        self.job_info = {
+            "calculator_type": calculator_type,
+            "mlearn_elements": mlearn_elements,
+        }
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger("MLearnForcesAnalyzer")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        self.logger.info(message)
+        print(message)
+
+    def setup_calculator(self):
+        return setup_calculator(self.calculator_type)
+
+    def run(self):
+        for element in self.mlearn_elements:
+            self.compare_mlearn_properties(element)
+
+    def compare_mlearn_properties(self, element):
+        """
+        Compare forces and stresses calculated by the FF calculator with mlearn DFT data for a given element.
+
+        Args:
+            element (str): Element symbol to filter structures (e.g., 'Si').
+        """
+        # Download the mlearn dataset if not already present
+        mlearn_zip_path = 'mlearn.json.zip'
+        if not os.path.isfile(mlearn_zip_path):
+            self.log("Downloading mlearn dataset...")
+            url = 'https://figshare.com/ndownloader/files/40357663'
+            response = requests.get(url)
+            with open(mlearn_zip_path, 'wb') as f:
+                f.write(response.content)
+            self.log("Download completed.")
+
+        # Read the JSON data from the zip file
+        with zipfile.ZipFile(mlearn_zip_path, 'r') as z:
+            with z.open('mlearn.json') as f:
+                mlearn_data = json.load(f)
+
+        # Convert mlearn data to DataFrame
+        df = pd.DataFrame(mlearn_data)
+
+        # Filter the dataset for the specified element
+        df['elements'] = df['atoms'].apply(lambda x: x['elements'])
+        df = df[df['elements'].apply(lambda x: element in x)]
+        df = df.reset_index(drop=True)
+        self.log(f"Filtered dataset to {len(df)} entries containing element '{element}'")
+
+        # Initialize lists to store results
+        force_results = []
+        stress_results = []
+
+        # Iterate over each structure
+        for idx, row in df.iterrows():
+            jid = row.get('jid', f"structure_{idx}")
+            atoms_dict = row['atoms']
+            atoms = Atoms.from_dict(atoms_dict)
+            dft_forces = np.array(row['forces'])
+            dft_stresses = np.array(row['stresses'])  # Original stresses in kBar
+
+            # Convert DFT stresses from kBar to GPa
+            dft_stresses_GPa = dft_stresses * -0.1  # kBar to GPa
+
+            # Convert DFT stresses to full 3x3 tensors
+            if dft_stresses_GPa.ndim == 1 and dft_stresses_GPa.size == 6:
+                dft_stress_tensor = voigt_6_to_full_3x3_stress(dft_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: DFT stresses not in expected format.")
+                continue  # Skip structures with unexpected stress format
+
+            # Calculate predicted properties
+            predicted_forces, predicted_stresses = self.calculate_properties(atoms)
+
+            # Convert predicted stresses from eV/ų to GPa
+            if predicted_stresses is not None and predicted_stresses.size == 6:
+                predicted_stresses_GPa = predicted_stresses * 160.21766208  # eV/ų to GPa
+                predicted_stress_tensor = voigt_6_to_full_3x3_stress(predicted_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: Predicted stresses not available.")
+                continue  # Skip structures where stresses are not available
+
+            # Flatten the 3x3 stress tensors to 9-component arrays for comparison
+            dft_stress_flat = dft_stress_tensor.flatten()
+            predicted_stress_flat = predicted_stress_tensor.flatten()
+
+            # Store the results
+            force_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_forces.flatten())),
+                'prediction': ';'.join(map(str, predicted_forces.flatten())),
+            })
+            stress_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_stress_flat)),
+                'prediction': ';'.join(map(str, predicted_stress_flat)),
+            })
+
+            # Optional: Progress indicator
+            if idx % 10 == 0:
+                self.log(f"Processed {idx + 1}/{len(df)} structures.")
+
+        # Ensure we have data to process
+        if not force_results or not stress_results:
+            self.log("No valid data found for forces or stresses. Exiting.")
+            return
+
+        # Save results to CSV files
+        force_df = pd.DataFrame(force_results)
+        force_csv = os.path.join(self.output_dir, f'AI-MLFF-forces-mlearn_{element}-test-multimae.csv')
+        force_df.to_csv(force_csv, index=False)
+        self.log(f"Saved force comparison data to '{force_csv}'")
+
+        stress_df = pd.DataFrame(stress_results)
+        stress_csv = os.path.join(self.output_dir, f'AI-MLFF-stresses-mlearn_{element}-test-multimae.csv')
+        stress_df.to_csv(stress_csv, index=False)
+        self.log(f"Saved stress comparison data to '{stress_csv}'")
+
+        # Zip the CSV files
+        self.zip_file(force_csv)
+        self.zip_file(stress_csv)
+
+        # Calculate error metrics
+        # Forces MAE
+        target_forces = np.concatenate(force_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_forces = np.concatenate(force_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        forces_mae = mean_absolute_error(target_forces, pred_forces)
+        self.log(f"Forces MAE for element '{element}': {forces_mae:.6f} eV/Å")
+
+        # Stresses MAE
+        target_stresses = np.concatenate(stress_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_stresses = np.concatenate(stress_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        stresses_mae = mean_absolute_error(target_stresses, pred_stresses)
+        self.log(f"Stresses MAE for element '{element}': {stresses_mae:.6f} GPa")
+
+        # Save MAE to job_info
+        self.job_info[f'forces_mae_{element}'] = forces_mae
+        self.job_info[f'stresses_mae_{element}'] = stresses_mae
+        self.save_job_info()
+
+        # Plot parity plots
+        forces_plot_filename = os.path.join(self.output_dir, f'forces_parity_plot_{element}.png')
+        self.plot_parity(target_forces, pred_forces, 'Forces', 'eV/Å', forces_plot_filename, element)
+
+        stresses_plot_filename = os.path.join(self.output_dir, f'stresses_parity_plot_{element}.png')
+        self.plot_parity(target_stresses, pred_stresses, 'Stresses', 'GPa', stresses_plot_filename, element)
+
+    def calculate_properties(self, atoms):
+        """
+        Calculate forces and stresses on the given atoms.
+
+        Returns:
+            Tuple of forces and stresses.
+        """
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Calculate properties
+        forces = ase_atoms.get_forces()
+        stresses = ase_atoms.get_stress()  # Voigt 6-component stress
+
+        return forces, stresses  # Return forces and stresses in Voigt notation
+
+    def plot_parity(self, target, prediction, property_name, units, filename, element):
+        """
+        Plot parity plot for a given property.
+
+        Args:
+            target (array-like): Target values.
+            prediction (array-like): Predicted values.
+            property_name (str): Name of the property (e.g., 'Forces').
+            units (str): Units of the property (e.g., 'eV/Å' or 'GPa').
+            filename (str): Filename to save the plot.
+            element (str): Element symbol.
+        """
+        plt.figure(figsize=(8, 8), dpi=300)
+        plt.scatter(target, prediction, alpha=0.5, edgecolors='k', s=20)
+        min_val = min(np.min(target), np.min(prediction))
+        max_val = max(np.max(target), np.max(prediction))
+        plt.plot([min_val, max_val], [min_val, max_val], 'k--', lw=2)
+        plt.xlabel(f'Target {property_name} ({units})', fontsize=14)
+        plt.ylabel(f'Predicted {property_name} ({units})', fontsize=14)
+        plt.title(f'Parity Plot for {property_name} - Element {element}', fontsize=16)
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(filename)
+        plt.close()
+        self.log(f"Saved parity plot for {property_name} as '{filename}'")
+
+    def zip_file(self, filename):
+        zip_filename = filename + '.zip'
+        with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
+            zf.write(filename, arcname=os.path.basename(filename))
+        os.remove(filename)  # Remove the original file
+        self.log(f"Zipped data to '{zip_filename}'")
+
+    def save_job_info(self):
+        job_info_filename = os.path.join(self.output_dir, f"mlearn_{self.calculator_type}_job_info.json")
+        with open(job_info_filename, 'w') as f:
+            json.dump(self.job_info, f, indent=4)
+
+
+#jid_list=['JVASP-1002']
+jid_list_all = [ 'JVASP-1002', 'JVASP-816', 'JVASP-867', 'JVASP-1029', 'JVASP-861','JVASP-30', 'JVASP-8169', 'JVASP-890', 'JVASP-8158','JVASP-8118',
+    'JVASP-107', 'JVASP-39', 'JVASP-7844', 'JVASP-35106', 'JVASP-1174',
+    'JVASP-1372', 'JVASP-91', 'JVASP-1186', 'JVASP-1408', 'JVASP-105410',
+    'JVASP-1177', 'JVASP-79204', 'JVASP-1393', 'JVASP-1312', 'JVASP-1327',
+    'JVASP-1183', 'JVASP-1192', 'JVASP-8003', 'JVASP-96', 'JVASP-1198',
+    'JVASP-1195', 'JVASP-9147', 'JVASP-41', 'JVASP-34674', 'JVASP-113',
+    'JVASP-32', 'JVASP-840', 'JVASP-21195', 'JVASP-981', 'JVASP-969',
+    'JVASP-802', 'JVASP-943', 'JVASP-14812', 'JVASP-984', 'JVASP-972',
+    'JVASP-958', 'JVASP-901', 'JVASP-1702', 'JVASP-931', 'JVASP-963',
+    'JVASP-95', 'JVASP-1201', 'JVASP-14837', 'JVASP-825', 'JVASP-966',
+    'JVASP-993', 'JVASP-23', 'JVASP-828', 'JVASP-1189', 'JVASP-810',
+    'JVASP-7630', 'JVASP-819', 'JVASP-1180', 'JVASP-837', 'JVASP-919',
+    'JVASP-7762', 'JVASP-934', 'JVASP-858', 'JVASP-895']
+#calculator_types = ["alignn_ff_aff307k_lmdb_param_low_rad_use_force_mult_mp_tak4","alignn_ff_v5.27.2024","alignn_ff_aff307k_kNN_2_2_128"]
+
+if __name__ == "__main__":
+    import pprint
+    parser = argparse.ArgumentParser(description="Run Materials Analyzer")
+    parser.add_argument("--input_file", default="input.json", type=str, help="Path to the input configuration JSON file")
+    args = parser.parse_args()
+
+    input_file = loadjson(args.input_file)
+    input_file_data = CHIPSFFConfig(**input_file)
+    pprint.pprint(input_file_data.dict())
+
+    # If film_id is provided, treat it as a list
+    film_jids = input_file_data.film_id if input_file_data.film_id else []
+
+    # If substrate_id is provided, treat it as a list
+    substrate_jids = input_file_data.substrate_id if input_file_data.substrate_id else []
+
+    # Case 1: Interface calculations with film_jid and substrate_jid
+    if film_jids and substrate_jids:
+        # Loop through all film and substrate JIDs and perform interface analysis
+        for film_jid, substrate_jid in zip(film_jids, substrate_jids):
+            print(f"Analyzing interface between {film_jid} and {substrate_jid} with {input_file_data.calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                calculator_type=input_file_data.calculator_type,
+                chemical_potentials_file=input_file_data.chemical_potentials_file,
+                film_jid=film_jid,
+                substrate_jid=substrate_jid,
+                film_index=input_file_data.film_index,
+                substrate_index=input_file_data.substrate_index,
+                bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+                phonon_settings=input_file_data.phonon_settings,
+                properties_to_calculate=input_file_data.properties_to_calculate,
+            )
+            analyzer.analyze_interfaces()
+
+    # Case 2: Single JID provided
+    elif input_file_data.jid and input_file_data.calculator_type:
+        print(f"Analyzing {input_file_data.jid} with {input_file_data.calculator_type}...")
+        analyzer = MaterialsAnalyzer(
+            jid=input_file_data.jid,
+            calculator_type=input_file_data.calculator_type,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+        analyzer.run_all()
+
+    # Case 3: Multiple JIDs and calculator types provided (batch processing)
+    elif input_file_data.jid_list and input_file_data.calculator_types:
+        analyze_multiple_structures(
+            jid_list=input_file_data.jid_list,
+            calculator_types=input_file_data.calculator_types,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+
+    elif input_file_data.mlearn_elements and input_file_data.calculator_type:
+        print(f"Running mlearn forces comparison for elements {input_file_data.mlearn_elements} with {input_file_data.calculator_type}...")
+        mlearn_analyzer = MLearnForcesAnalyzer(
+            calculator_type=input_file_data.calculator_type,
+            mlearn_elements=input_file_data.mlearn_elements
+        )
+        mlearn_analyzer.run()
+    else:
+        print("Please provide valid arguments in the configuration file.")
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/AI-MLFF-forces-mlearnall_Mo-test-multimae.csv.zip b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Mo-test-multimae.csv.zip
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/AI-MLFF-forces-mlearnall_Mo-test-multimae.csv.zip
rename to jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Mo-test-multimae.csv.zip
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/metadata.json
new file mode 100644
index 000000000..93f5c9fd0
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/metadata.json
@@ -0,0 +1,15 @@
+{
+    "model_name": "CHIPS-FF",
+    "project_url": "https://github.com/usnistgov/chipsff",
+    "date_submitted": "11-22-2024",
+    "author_email": "daniel.wines@nist.gov",
+    "time_taken_seconds": {},
+    "team_name": "JARVIS",
+    "language": "python",
+    "os": "linux",
+    "software_used": "jarvis-tools,numpy,scipy,torch,alignn",
+    "hardware_used": "mr-french at NIST",
+    "git_url": [
+        "https://github.com/usnistgov/chipsff"
+    ]
+}
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/mlearn_alignn_ff_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/mlearn_alignn_ff_job_info.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/mlearn_analysis_log.txt
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/mlearn_analysis_log.txt
rename to jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/mlearn_analysis_log.txt
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/run.sh
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/run.sh
rename to jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/run.sh
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/run_chipsff.py
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff/run_chipsff.py
rename to jarvis_leaderboard/contributions/mlearn_analysis_Mo_alignn_ff_5_27_24/run_chipsff.py
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diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/metadata.json
new file mode 100644
index 000000000..93f5c9fd0
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/metadata.json
@@ -0,0 +1,15 @@
+{
+    "model_name": "CHIPS-FF",
+    "project_url": "https://github.com/usnistgov/chipsff",
+    "date_submitted": "11-22-2024",
+    "author_email": "daniel.wines@nist.gov",
+    "time_taken_seconds": {},
+    "team_name": "JARVIS",
+    "language": "python",
+    "os": "linux",
+    "software_used": "jarvis-tools,numpy,scipy,torch,alignn",
+    "hardware_used": "mr-french at NIST",
+    "git_url": [
+        "https://github.com/usnistgov/chipsff"
+    ]
+}
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
new file mode 100644
index 000000000..fb20a9fd8
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
@@ -0,0 +1,8 @@
+{
+    "calculator_type": "alignn_ff_12_2_24",
+    "mlearn_elements": [
+        "Ni"
+    ],
+    "forces_mae_Ni": 0.3026297083080123,
+    "stresses_mae_Ni": 2.70995814275235
+}
\ No newline at end of file
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/mlearn_analysis_log.txt
new file mode 100644
index 000000000..67ac42d1e
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/mlearn_analysis_log.txt
@@ -0,0 +1,39 @@
+2024-12-03 16:35:00,383 - INFO - Filtered dataset to 294 entries containing element 'Ni'
+2024-12-03 16:35:07,354 - INFO - Processed 1/294 structures.
+2024-12-03 16:35:14,244 - INFO - Processed 11/294 structures.
+2024-12-03 16:35:21,192 - INFO - Processed 21/294 structures.
+2024-12-03 16:35:28,133 - INFO - Processed 31/294 structures.
+2024-12-03 16:35:32,100 - INFO - Processed 41/294 structures.
+2024-12-03 16:35:36,186 - INFO - Processed 51/294 structures.
+2024-12-03 16:35:44,447 - INFO - Processed 61/294 structures.
+2024-12-03 16:35:52,440 - INFO - Processed 71/294 structures.
+2024-12-03 16:35:59,821 - INFO - Processed 81/294 structures.
+2024-12-03 16:36:08,288 - INFO - Processed 91/294 structures.
+2024-12-03 16:36:16,605 - INFO - Processed 101/294 structures.
+2024-12-03 16:36:24,527 - INFO - Processed 111/294 structures.
+2024-12-03 16:36:32,506 - INFO - Processed 121/294 structures.
+2024-12-03 16:36:39,524 - INFO - Processed 131/294 structures.
+2024-12-03 16:36:47,049 - INFO - Processed 141/294 structures.
+2024-12-03 16:36:54,999 - INFO - Processed 151/294 structures.
+2024-12-03 16:37:01,881 - INFO - Processed 161/294 structures.
+2024-12-03 16:37:06,612 - INFO - Processed 171/294 structures.
+2024-12-03 16:37:10,791 - INFO - Processed 181/294 structures.
+2024-12-03 16:37:15,775 - INFO - Processed 191/294 structures.
+2024-12-03 16:37:21,431 - INFO - Processed 201/294 structures.
+2024-12-03 16:37:27,026 - INFO - Processed 211/294 structures.
+2024-12-03 16:37:33,583 - INFO - Processed 221/294 structures.
+2024-12-03 16:37:40,278 - INFO - Processed 231/294 structures.
+2024-12-03 16:37:46,881 - INFO - Processed 241/294 structures.
+2024-12-03 16:37:53,463 - INFO - Processed 251/294 structures.
+2024-12-03 16:37:59,923 - INFO - Processed 261/294 structures.
+2024-12-03 16:38:05,245 - INFO - Processed 271/294 structures.
+2024-12-03 16:38:13,378 - INFO - Processed 281/294 structures.
+2024-12-03 16:38:19,741 - INFO - Processed 291/294 structures.
+2024-12-03 16:38:21,750 - INFO - Saved force comparison data to 'mlearn_analysis_Ni_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Ni-test-multimae.csv'
+2024-12-03 16:38:21,757 - INFO - Saved stress comparison data to 'mlearn_analysis_Ni_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Ni-test-multimae.csv'
+2024-12-03 16:38:22,010 - INFO - Zipped data to 'mlearn_analysis_Ni_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Ni-test-multimae.csv.zip'
+2024-12-03 16:38:22,020 - INFO - Zipped data to 'mlearn_analysis_Ni_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Ni-test-multimae.csv.zip'
+2024-12-03 16:38:22,065 - INFO - Forces MAE for element 'Ni': 0.302630 eV/Å
+2024-12-03 16:38:22,070 - INFO - Stresses MAE for element 'Ni': 2.709958 GPa
+2024-12-03 16:38:25,617 - INFO - Saved parity plot for Forces as 'mlearn_analysis_Ni_alignn_ff_12_2_24/forces_parity_plot_Ni.png'
+2024-12-03 16:38:26,305 - INFO - Saved parity plot for Stresses as 'mlearn_analysis_Ni_alignn_ff_12_2_24/stresses_parity_plot_Ni.png'
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/run.sh
new file mode 100644
index 000000000..0e331ca03
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/run.sh
@@ -0,0 +1,148 @@
+#!/bin/bash
+
+# Create logs directory if it doesn't exist
+mkdir -p logs
+
+# Define arrays of JIDs, calculators, and mlearn elements
+calculator_types=("alignn_ff_12_2_24")
+mlearn_elements=("Si" "Ge" "Cu" "Ni" "Li" "Mo")
+
+# Function to submit a job for bulk material calculations
+submit_bulk_job() {
+  local jid=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=30-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=${jid}_${calculator}
+#SBATCH --output=logs/${jid}_${calculator}_%j.out
+#SBATCH --error=logs/${jid}_${calculator}_%j.err
+
+# Generate input JSON file for this combination
+cat > input_${jid}_${calculator}.json <<JSON
+{
+  "jid": "$jid",
+  "calculator_type": "$calculator",
+  "chemical_potentials_file": "chemical_potentials.json",
+  "properties_to_calculate": [
+    "relax_structure",
+    "calculate_ev_curve",
+    "calculate_formation_energy",
+    "calculate_elastic_tensor",
+    "run_phonon_analysis",
+    "analyze_surfaces",
+    "analyze_defects",
+    "run_phonon3_analysis",
+    "general_melter",
+    "calculate_rdf"
+  ],
+  "bulk_relaxation_settings": {
+    "filter_type": "ExpCellFilter",
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": false
+    }
+  },
+  "phonon_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "use_conventional_cell": true,
+  "surface_settings": {
+    "indices_list": [
+      [0, 1, 0],
+      [0, 0, 1]
+    ],
+    "layers": 4,
+    "vacuum": 18,
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "defect_settings": {
+    "generate_settings": {
+      "on_conventional_cell": true,
+      "enforce_c_size": 8,
+      "extend": 1
+    },
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "phonon3_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "md_settings": {
+    "dt": 1,
+    "temp0": 35,
+    "nsteps0": 10,
+    "temp1": 200,
+    "nsteps1": 20,
+    "taut": 20,
+    "min_size": 10.0
+  }
+}
+JSON
+
+# Run the Python analysis for this JID/calculator combination
+python run_chipsff.py --input_file input_${jid}_${calculator}.json
+
+EOT
+}
+
+# Function to submit a job for mlearn analysis
+submit_mlearn_job() {
+  local element=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=1-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=mlearn_${element}_${calculator}
+#SBATCH --output=logs/mlearn_${element}_${calculator}_%j.out
+#SBATCH --error=logs/mlearn_${element}_${calculator}_%j.err
+
+# Generate input JSON file for this mlearn element/calculator combination
+cat > input_mlearn_${element}_${calculator}.json <<JSON
+{
+  "mlearn_elements": ["$element"],
+  "calculator_type": "$calculator",
+  "properties_to_calculate": ["compare_mlearn_forces"]
+}
+JSON
+
+# Run the Python analysis for this mlearn element/calculator combination
+python run_chipsff.py --input_file input_mlearn_${element}_${calculator}.json
+
+EOT
+}
+
+# Loop through each JID and calculator combination for bulk calculations
+for jid in "${jid_list[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_bulk_job "$jid" "$calculator"
+  done
+done
+
+# Loop through each mlearn element and calculator combination for mlearn analysis
+for element in "${mlearn_elements[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_mlearn_job "$element" "$calculator"
+  done
+done
+
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/run_chipsff.py
new file mode 100644
index 000000000..79b65eb07
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_12_2_24/run_chipsff.py
@@ -0,0 +1,2286 @@
+#!/usr/bin/env python
+import os
+import subprocess
+import sys
+import time
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from ase.eos import EquationOfState
+from ase import Atoms as AseAtoms
+from ase.units import kJ
+from ase.constraints import ExpCellFilter
+from ase.optimize.fire import FIRE
+from ase.stress import voigt_6_to_full_3x3_stress
+import ase.units
+from jarvis.db.figshare import get_jid_data, data, get_request_data
+from jarvis.core.atoms import Atoms, ase_to_atoms
+from jarvis.io.vasp.inputs import Poscar
+from jarvis.core.kpoints import Kpoints3D as Kpoints
+from jarvis.analysis.defects.vacancy import Vacancy
+from jarvis.analysis.defects.surface import Surface
+import pandas as pd
+import h5py
+import shutil
+import glob
+import io
+import contextlib
+import requests
+import zipfile
+import re
+import plotly.express as px
+from sklearn.metrics import mean_absolute_error, r2_score
+from matplotlib.gridspec import GridSpec
+import argparse
+from jarvis.db.jsonutils import loadjson
+from chipsff.config import CHIPSFFConfig
+
+dft_3d = data("dft_3d")
+vacancydb = data("vacancydb")
+surf_url = "https://figshare.com/ndownloader/files/46355689"
+surface_data = get_request_data(js_tag="surface_db_dd.json", url=surf_url)
+
+def get_entry(jid):
+    for entry in dft_3d:
+        if entry["jid"] == jid:
+            return entry
+    raise ValueError(f"JID {jid} not found in the database")
+
+def collect_data(dft_3d, vacancydb, surface_data):
+    defect_ids = list(set([entry["jid"] for entry in vacancydb]))
+    surf_ids = list(set([entry["name"].split("Surface-")[1].split("_miller_")[0] for entry in surface_data]))
+
+    aggregated_data = []
+    for entry in dft_3d:
+        tmp = entry
+        tmp["vacancy"] = {}
+        tmp["surface"] = {}
+
+        # Check if the entry is in the defect dataset
+        if entry["jid"] in defect_ids:
+            for vac_entry in vacancydb:
+                if entry["jid"] == vac_entry["jid"]:
+                    tmp["vacancy"].setdefault(
+                        vac_entry["id"].split("_")[0] + "_" + vac_entry["id"].split("_")[1],
+                        vac_entry["ef"]
+                    )
+
+        # Check if the entry is in the surface dataset
+        if entry["jid"] in surf_ids:
+            for surf_entry in surface_data:
+                jid = surf_entry["name"].split("Surface-")[1].split("_miller_")[0]
+                if entry["jid"] == jid:
+                    tmp["surface"].setdefault(
+                        "_".join(surf_entry["name"].split("_thickness")[0].split("_")[0:5]),
+                        surf_entry["surf_en"]
+                    )
+
+        aggregated_data.append(tmp)
+    
+    return aggregated_data
+
+def get_vacancy_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the vacancy formation energy entry (vac_en_entry) for a given jid.
+    
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+    
+    Returns:
+    dict: A dictionary containing the vacancy formation energy entry and corresponding symbol.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            vacancy_data = entry.get("vacancy", {})
+            if vacancy_data:
+                return [{"symbol": key, "vac_en_entry": value} for key, value in vacancy_data.items()]
+            else:
+                return f"No vacancy data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+    
+def get_surface_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the surface energy entry (surf_en_entry) for a given jid.
+
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+
+    Returns:
+    list: A list of dictionaries containing the surface energy entry and corresponding name.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            surface_data = entry.get("surface", {})
+            if surface_data:
+                # Prepend 'Surface-JVASP-<jid>_' to the key for correct matching
+                return [{"name": f"{key}", "surf_en_entry": value} for key, value in surface_data.items()]
+            else:
+                return f"No surface data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+
+
+def log_job_info(message, log_file):
+    """Log job information to a file and print it."""
+    with open(log_file, "a") as f:
+        f.write(message + "\n")
+    print(message)
+
+def save_dict_to_json(data_dict, filename):
+    with open(filename, "w") as f:
+        json.dump(data_dict, f, indent=4)
+
+def load_dict_from_json(filename):
+    with open(filename, 'r') as f:
+        return json.load(f)
+        
+def setup_calculator(calculator_type):
+    if calculator_type == "matgl":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "matgl-direct":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-DIRECT-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "alignn_ff":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+        model_path = default_path() #can be adjusted to other ALIGNN models
+        return AlignnAtomwiseCalculator(
+            path=model_path,
+            stress_wt=0.1,
+            force_mult_natoms=False,
+            force_multiplier=1,
+            modl_filename="best_model.pt",
+        )
+    elif calculator_type == "alignn_ff_12_2_24":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+        return AlignnAtomwiseCalculator()
+    elif calculator_type == "chgnet":
+        from chgnet.model.dynamics import CHGNetCalculator
+        return CHGNetCalculator()
+    elif calculator_type == "mace":
+        from mace.calculators import mace_mp
+        return mace_mp()
+    elif calculator_type == "mace-alexandria":
+        from mace.calculators.mace import MACECalculator
+        model_path="/users/dtw2/utils/models/alexandria_v2/mace/2D_universal_force_field_cpu.model" #adjust path to mace-alexandria
+        return MACECalculator(model_path,device="cpu") 
+    elif calculator_type == "sevennet":
+        from sevenn.sevennet_calculator import SevenNetCalculator
+        checkpoint_path = "/users/dtw2/SevenNet/pretrained_potentials/SevenNet_0__11July2024/checkpoint_sevennet_0.pth" #adjust path to sevennet
+        return SevenNetCalculator(checkpoint_path, device="cpu")
+    elif calculator_type == "orb-v2":
+        from orb_models.forcefield import pretrained
+        from orb_models.forcefield.calculator import ORBCalculator
+        orbff = pretrained.orb_v2()
+        return ORBCalculator(orbff, device="cpu")
+    elif calculator_type == "eqV2_31M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat.pt") #adjust path to OMat24   
+    elif calculator_type == "eqV2_153M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_153M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_31M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat_mp_salex.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat_mp_salex.pt") #adjust path to OMat24
+    else:
+        raise ValueError("Unsupported calculator type")
+
+class MaterialsAnalyzer:
+    def __init__(
+        self,
+        jid=None,
+        calculator_type=None,
+        chemical_potentials_file=None,
+        film_jid=None,
+        substrate_jid=None,
+        film_index=None,
+        substrate_index=None,
+        bulk_relaxation_settings=None,
+        phonon_settings=None,
+        properties_to_calculate=None,
+        use_conventional_cell=False,
+        surface_settings=None,
+        defect_settings=None,
+        phonon3_settings=None,
+        md_settings=None
+    ):
+        self.calculator_type = calculator_type
+        self.use_conventional_cell = use_conventional_cell
+        self.chemical_potentials_file = chemical_potentials_file
+        self.bulk_relaxation_settings = bulk_relaxation_settings or {}
+        self.phonon_settings = phonon_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.properties_to_calculate = properties_to_calculate or []
+        self.surface_settings = surface_settings or {}
+        self.defect_settings = defect_settings or {}
+        self.film_index = film_index or "1_1_0"
+        self.substrate_index = substrate_index or "1_1_0"
+        self.phonon3_settings = phonon3_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.md_settings = md_settings or {'dt': 1, 'temp0': 3500, 'nsteps0': 1000, 'temp1': 300, 'nsteps1': 2000, 'taut': 20, 'min_size': 10.0}
+        if jid:
+            self.jid = jid
+            # Load atoms for the given JID
+            self.atoms = self.get_atoms(jid)
+            # Get reference data for the material
+            self.reference_data = get_entry(jid)
+            # Set up the output directory and log file
+            self.output_dir = f"{jid}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(self.output_dir, f"{jid}_job_log.txt")
+            # Initialize job_info dictionary
+            self.job_info = {
+                "jid": jid,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        elif film_jid and substrate_jid:
+            # Ensure film_jid and substrate_jid are strings, not lists
+            if isinstance(film_jid, list):
+                film_jid = film_jid[0]
+            if isinstance(substrate_jid, list):
+                substrate_jid = substrate_jid[0]
+
+            self.film_jid = film_jid
+            self.substrate_jid = substrate_jid
+
+            # Include Miller indices in directory and file names
+            self.output_dir = f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(
+                self.output_dir,
+                f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_job_log.txt",
+            )
+            self.job_info = {
+                "film_jid": film_jid,
+                "substrate_jid": substrate_jid,
+                "film_index": self.film_index,
+                "substrate_index": self.substrate_index,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        else:
+            raise ValueError("Either 'jid' or both 'film_jid' and 'substrate_jid' must be provided.")
+
+        # Set up the logger
+        self.setup_logger()
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger(self.jid or f"{self.film_jid}_{self.substrate_jid}")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        """Log information to the job log file."""
+        log_job_info(message, self.log_file)
+
+    def get_atoms(self, jid):
+        dat = get_jid_data(jid=jid, dataset="dft_3d")
+        return Atoms.from_dict(dat["atoms"])
+
+    def setup_calculator(self):
+        calc = setup_calculator(self.calculator_type)
+        self.log(f"Using calculator: {self.calculator_type}")
+        return calc
+
+    def load_chemical_potentials(self):
+        if os.path.exists(self.chemical_potentials_file):
+            with open(self.chemical_potentials_file, "r") as f:
+                return json.load(f)
+        else:
+            return {}
+
+    def save_chemical_potentials(self):
+        with open(self.chemical_potentials_file, "w") as f:
+            json.dump(self.chemical_potentials, f, indent=4)
+
+    def capture_fire_output(self, ase_atoms, fmax, steps):
+        """Capture the output of the FIRE optimizer."""
+        log_stream = io.StringIO()
+        with contextlib.redirect_stdout(log_stream):
+            dyn = FIRE(ase_atoms)
+            dyn.run(fmax=fmax, steps=steps)
+        output = log_stream.getvalue().strip()
+
+        final_energy = None
+        if output:
+            last_line = output.split("\n")[-1]
+            match = re.search(r"FIRE:\s+\d+\s+\d+:\d+:\d+\s+(-?\d+\.\d+)", last_line)
+            if match:
+                final_energy = float(match.group(1))
+
+        return final_energy, dyn.nsteps
+
+    def relax_structure(self):
+        """Perform structure relaxation and log the process."""
+        self.log(f"Starting relaxation for {self.jid}")
+
+        # Use conventional cell if specified
+
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for relaxation.")
+            self.atoms = self.atoms.get_conventional_atoms  # or appropriate method
+
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.bulk_relaxation_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.bulk_relaxation_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', False)
+        ase_atoms = self.atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+
+        # Run FIRE optimizer and capture the output using relaxation settings
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < steps
+
+        # Log the final energy and relaxation status
+        self.log(f"Final energy of FIRE optimization for structure: {final_energy}")
+        self.log(
+            f"Relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        # Update job info and save the relaxed structure
+        self.job_info["relaxed_atoms"] = relaxed_atoms.to_dict()
+        self.job_info["final_energy_structure"] = final_energy
+        self.job_info["converged"] = converged
+        self.log(f"Relaxed structure: {relaxed_atoms.to_dict()}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return relaxed_atoms if converged else None
+
+
+
+    def calculate_formation_energy(self, relaxed_atoms):
+        """
+        Calculate the formation energy per atom using the equilibrium energy and chemical potentials.
+        """
+        e0 = self.job_info["equilibrium_energy"]
+        composition = relaxed_atoms.composition.to_dict()
+        total_energy = e0
+
+        for element, amount in composition.items():
+            chemical_potential = self.get_chemical_potential(element)
+            if chemical_potential is None:
+                self.log(f"Skipping formation energy calculation due to missing chemical potential for {element}.")
+                continue  # Or handle this appropriately
+            total_energy -= chemical_potential * amount
+
+        formation_energy_per_atom = total_energy / relaxed_atoms.num_atoms
+
+    # Log and save the formation energy
+        self.job_info["formation_energy_per_atom"] = formation_energy_per_atom
+        self.log(f"Formation energy per atom: {formation_energy_per_atom}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return formation_energy_per_atom
+
+    def calculate_element_chemical_potential(self, element, element_jid):
+        """
+        Calculate the chemical potential of a pure element using its standard structure.
+        """
+        self.log(f"Calculating chemical potential for element: {element} using JID: {element_jid}")
+        try:
+            # Get standard structure for the element using the provided JID
+            element_atoms = self.get_atoms(element_jid)
+            ase_atoms = element_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+
+            # Perform energy calculation
+            energy = ase_atoms.get_potential_energy() / len(ase_atoms)
+            self.log(f"Calculated chemical potential for {element}: {energy} eV/atom")
+            return energy
+        except Exception as e:
+            self.log(f"Error calculating chemical potential for {element}: {e}")
+            return None
+
+    def get_chemical_potential(self, element):
+        """Fetch chemical potential from JSON based on the element and calculator."""
+        element_data = self.chemical_potentials.get(element, {})
+        chemical_potential = element_data.get(f"energy_{self.calculator_type}")
+
+        if chemical_potential is None:
+            self.log(
+                f"No chemical potential found for {element} with calculator {self.calculator_type}, calculating it now..."
+            )
+            # Get standard JID for the element from chemical_potentials.json
+            element_jid = element_data.get("jid")
+            if element_jid is None:
+                self.log(f"No standard JID found for element {element} in chemical_potentials.json")
+                return None  # Skip this element
+
+            # Calculate chemical potential
+            chemical_potential = self.calculate_element_chemical_potential(element, element_jid)
+            if chemical_potential is None:
+                self.log(f"Failed to calculate chemical potential for {element}")
+                return None
+            # Add it to the chemical potentials dictionary
+            if element not in self.chemical_potentials:
+                self.chemical_potentials[element] = {}
+            self.chemical_potentials[element][f"energy_{self.calculator_type}"] = chemical_potential
+            # Save the updated chemical potentials to file
+            self.save_chemical_potentials()
+
+        return chemical_potential
+    def calculate_forces(self, atoms):
+        """
+        Calculate the forces on the given atoms without performing relaxation.
+        """
+        self.log(f"Calculating forces for {self.jid}")
+
+    # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+    # Calculate forces
+        forces = ase_atoms.get_forces()  # This returns an array of forces
+
+    # Log and save the forces
+        self.job_info['forces'] = forces.tolist()  # Convert to list for JSON serialization
+        self.log(f"Forces calculated: {forces}")
+
+    # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return forces
+
+    def calculate_ev_curve(self, relaxed_atoms):
+        """Calculate the energy-volume (E-V) curve and log results."""
+        self.log(f"Calculating EV curve for {self.jid}")
+
+        dx = np.arange(-0.06, 0.06, 0.01)  # Strain values
+        y = []  # Energies
+        vol = []  # Volumes
+        strained_structures = []  # To store strained structures
+
+        for i in dx:
+            # Apply strain and calculate energy at each strain value
+            strained_atoms = relaxed_atoms.strain_atoms(i)
+            strained_structures.append(strained_atoms)
+            ase_atoms = strained_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+            energy = ase_atoms.get_potential_energy()
+
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+        # Convert data to numpy arrays for processing
+        y = np.array(y)
+        vol = np.array(vol)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        try:
+            eos = EquationOfState(vol, y, eos="murnaghan")
+            v0, e0, B = eos.fit()
+
+            # Bulk modulus in GPa (conversion factor from ASE units)
+            kv = B / kJ * 1.0e24  # Convert to GPa
+
+            # Log important results
+            self.log(f"Bulk modulus: {kv} GPa")
+            self.log(f"Equilibrium energy: {e0} eV")
+            self.log(f"Equilibrium volume: {v0} ų")
+
+            # Save E-V curve plot
+            fig = plt.figure()
+            eos.plot()
+            ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+            fig.savefig(ev_plot_filename)
+            plt.close(fig)
+            self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+            # Save E-V curve data to a text file
+            ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+            with open(ev_data_filename, "w") as f:
+                f.write("Volume (ų)\tEnergy (eV)\n")
+                for v, e in zip(vol, y):
+                    f.write(f"{v}\t{e}\n")
+            self.log(f"E-V curve data saved to {ev_data_filename}")
+
+            # Update job info with the results
+            self.job_info["bulk_modulus"] = kv
+            self.job_info["equilibrium_energy"] = e0
+            self.job_info["equilibrium_volume"] = v0
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        except RuntimeError as e:
+            self.log(f"Error fitting EOS for {self.jid}: {e}")
+            self.log("Skipping bulk modulus calculation due to fitting error.")
+            kv = None  # Set bulk modulus to None or handle this as you wish
+            e0, v0 = None, None  # Set equilibrium energy and volume to None
+
+        # Return additional values for thermal expansion analysis
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+
+    def calculate_elastic_tensor(self, relaxed_atoms):
+        import elastic
+        from elastic import get_elementary_deformations, get_elastic_tensor
+        """
+        Calculate the elastic tensor for the relaxed structure using the provided calculator.
+        """
+        self.log(f"Starting elastic tensor calculation for {self.jid}")
+        start_time = time.time()
+
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Get elementary deformations for elastic tensor calculation
+        systems = elastic.get_elementary_deformations(ase_atoms)
+
+        # Calculate the elastic tensor and convert to GPa
+        cij_order = elastic.elastic.get_cij_order(ase_atoms)
+        Cij, Bij = elastic.get_elastic_tensor(ase_atoms, systems)
+        elastic_tensor = {
+            i: j / ase.units.GPa for i, j in zip(cij_order, Cij)
+        }  # Convert to GPa
+
+        # Save and log the results
+        self.job_info["elastic_tensor"] = elastic_tensor
+        self.log(
+            f"Elastic tensor for {self.jid} with {self.calculator_type}: {elastic_tensor}"
+        )
+
+        # Timing the calculation
+        end_time = time.time()
+        self.log(f"Elastic Calculation time: {end_time - start_time} seconds")
+
+        # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return elastic_tensor
+
+    def run_phonon_analysis(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Perform Phonon calculation, generate force constants, and plot band structure & DOS."""
+        self.log(f"Starting phonon analysis for {self.jid}")
+        phonopy_bands_figname = f"ph_{self.jid}_{self.calculator_type}.png"
+
+        # Phonon generation parameters
+        dim = self.phonon_settings.get('dim', [2, 2, 2])
+        # Define the conversion factor from THz to cm^-1
+        THz_to_cm = 33.35641  # 1 THz = 33.35641 cm^-1
+
+        force_constants_filename = "FORCE_CONSTANTS"
+        eigenvalues_filename = "phonon_eigenvalues.txt"
+        thermal_props_filename = "thermal_properties.txt"
+        write_fc = True
+        min_freq_tol_cm = -5.0  # in cm^-1
+        distance = self.phonon_settings.get('distance', 0.2)
+
+        # Generate k-point path
+        kpoints = Kpoints().kpath(relaxed_atoms, line_density=5)
+
+        # Convert atoms to Phonopy-compatible object
+        self.log("Converting atoms to Phonopy-compatible format...")
+        bulk = relaxed_atoms.phonopy_converter()
+        phonon = Phonopy(
+            bulk,
+            [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]],
+            # Do not set factor here to keep frequencies in THz during calculations
+        )
+
+        # Generate displacements
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+        self.log(f"Generated {len(supercells)} supercells for displacements.")
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for idx, scell in enumerate(supercells):
+            self.log(f"Calculating forces for supercell {idx+1}...")
+            ase_atoms = AseAtoms(
+                symbols=scell.symbols,
+                positions=scell.positions,
+                cell=scell.cell,
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+
+            # Correct for drift force
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+
+            set_of_forces.append(forces)
+
+        # Generate force constants
+        self.log("Producing force constants...")
+        phonon.produce_force_constants(forces=set_of_forces)
+
+        # Write force constants to file if required
+        if write_fc:
+            force_constants_filepath = os.path.join(
+                self.output_dir, force_constants_filename
+            )
+            self.log(f"Writing force constants to {force_constants_filepath}...")
+            write_FORCE_CONSTANTS(
+                phonon.force_constants, filename=force_constants_filepath
+            )
+            self.log(f"Force constants saved to {force_constants_filepath}")
+
+        # Prepare bands for band structure calculation
+        bands = [kpoints.kpts]  # Assuming kpoints.kpts is a list of q-points
+
+        # Prepare labels and path_connections
+        labels = []
+        path_connections = []
+        for i, label in enumerate(kpoints.labels):
+            if label:
+                labels.append(label)
+            else:
+                labels.append("")  # Empty string for points without labels
+
+        # Since we have a single path, set path_connections accordingly
+        path_connections = [True] * (len(bands) - 1)
+        path_connections.append(False)
+
+        # Run band structure calculation with labels
+        self.log("Running band structure calculation...")
+        phonon.run_band_structure(
+            bands,
+            with_eigenvectors=False,
+            labels=labels,
+            path_connections=path_connections,
+        )
+
+        # Write band.yaml file (frequencies will be in THz)
+        band_yaml_filepath = os.path.join(self.output_dir, "band.yaml")
+        self.log(f"Writing band structure data to {band_yaml_filepath}...")
+        phonon.band_structure.write_yaml(filename=band_yaml_filepath)
+        self.log(f"band.yaml saved to {band_yaml_filepath}")
+
+        # --- Begin post-processing to convert frequencies to cm^-1 while preserving formatting ---
+        from ruamel.yaml import YAML
+
+        self.log(f"Converting frequencies in {band_yaml_filepath} to cm^-1 while preserving formatting...")
+        yaml = YAML()
+        yaml.preserve_quotes = True
+
+        with open(band_yaml_filepath, 'r') as f:
+            band_data = yaml.load(f)
+
+        for phonon_point in band_data['phonon']:
+            for band in phonon_point['band']:
+                freq = band['frequency']
+                if freq is not None:
+                    band['frequency'] = freq * THz_to_cm
+
+        with open(band_yaml_filepath, 'w') as f:
+            yaml.dump(band_data, f)
+
+        self.log(f"Frequencies in {band_yaml_filepath} converted to cm^-1 with formatting preserved")
+        # --- End post-processing ---
+
+        # Phonon band structure and eigenvalues
+        lbls = kpoints.labels
+        lbls_ticks = []
+        freqs = []
+        lbls_x = []
+        count = 0
+        eigenvalues = []
+
+        for ii, k in enumerate(kpoints.kpts):
+            k_str = ",".join(map(str, k))
+            if ii == 0 or k_str != ",".join(map(str, kpoints.kpts[ii - 1])):
+                freqs_at_k = phonon.get_frequencies(k)  # Frequencies in THz
+                freqs_at_k_cm = freqs_at_k * THz_to_cm  # Convert to cm^-1
+                freqs.append(freqs_at_k_cm)
+                eigenvalues.append((k, freqs_at_k_cm))  # Store frequencies in cm^-1
+                lbl = "$" + str(lbls[ii]) + "$" if lbls[ii] else ""
+                if lbl:
+                    lbls_ticks.append(lbl)
+                    lbls_x.append(count)
+                count += 1
+
+        # Write eigenvalues to file with frequencies in cm^-1
+        eigenvalues_filepath = os.path.join(self.output_dir, eigenvalues_filename)
+        self.log(f"Writing phonon eigenvalues to {eigenvalues_filepath}...")
+        with open(eigenvalues_filepath, "w") as eig_file:
+            eig_file.write("k-points\tFrequencies (cm^-1)\n")
+            for k, freqs_at_k_cm in eigenvalues:
+                k_str = ",".join(map(str, k))
+                freqs_str = "\t".join(map(str, freqs_at_k_cm))
+                eig_file.write(f"{k_str}\t{freqs_str}\n")
+        self.log(f"Phonon eigenvalues saved to {eigenvalues_filepath}")
+
+        # Convert frequencies to numpy array in cm^-1
+        freqs = np.array(freqs)
+
+        # Plot phonon band structure and DOS
+        the_grid = plt.GridSpec(1, 2, width_ratios=[3, 1], wspace=0.0)
+        plt.rcParams.update({"font.size": 18})
+        plt.figure(figsize=(10, 5))
+
+        # Plot phonon bands
+        plt.subplot(the_grid[0])
+        for i in range(freqs.shape[1]):
+            plt.plot(freqs[:, i], lw=2, c="b")
+        for i in lbls_x:
+            plt.axvline(x=i, c="black")
+        plt.xticks(lbls_x, lbls_ticks)
+        plt.ylabel("Frequency (cm$^{-1}$)")
+        plt.xlim([0, max(lbls_x)])
+
+        # Run mesh and DOS calculations
+        phonon.run_mesh([40, 40, 40], is_gamma_center=True, is_mesh_symmetry=False)
+        phonon.run_total_dos()
+        tdos = phonon.total_dos
+        freqs_dos = np.array(tdos.frequency_points) * THz_to_cm  # Convert to cm^-1
+        dos_values = tdos.dos
+        min_freq = min_freq_tol_cm  # in cm^-1
+        max_freq = max(freqs_dos)
+
+        plt.ylim([min_freq, max_freq])
+
+        # Plot DOS
+        plt.subplot(the_grid[1])
+        plt.fill_between(
+            dos_values, freqs_dos, color=(0.2, 0.4, 0.6, 0.6), edgecolor="k", lw=1, y2=0
+        )
+        plt.xlabel("DOS")
+        plt.yticks([])
+        plt.xticks([])
+        plt.ylim([min_freq, max_freq])
+        plt.xlim([0, max(dos_values)])
+
+        # Save the plot
+        os.makedirs(self.output_dir, exist_ok=True)
+        plot_filepath = os.path.join(self.output_dir, phonopy_bands_figname)
+        plt.tight_layout()
+        plt.savefig(plot_filepath)
+        self.log(
+            f"Phonon band structure and DOS combined plot saved to {plot_filepath}"
+        )
+        plt.close()
+
+        self.log("Calculating thermal properties...")
+        phonon.run_mesh(mesh=[20, 20, 20])
+        phonon.run_thermal_properties(t_step=10, t_max=1000, t_min=0)
+        tprop_dict = phonon.get_thermal_properties_dict()
+
+        # Plot thermal properties
+        plt.figure()
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["free_energy"],
+            label="Free energy (kJ/mol)",
+            color="red",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["entropy"],
+            label="Entropy (J/K*mol)",
+            color="blue",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["heat_capacity"],
+            label="Heat capacity (J/K*mol)",
+            color="green",
+        )
+        plt.legend()
+        plt.xlabel("Temperature (K)")
+        plt.ylabel("Thermal Properties")
+        plt.title("Thermal Properties")
+
+        thermal_props_plot_filepath = os.path.join(
+            self.output_dir, f"Thermal_Properties_{self.jid}.png"
+        )
+        plt.savefig(thermal_props_plot_filepath)
+        self.log(f"Thermal properties plot saved to {thermal_props_plot_filepath}")
+        plt.close()
+
+        # Save thermal properties to file
+        thermal_props_filepath = os.path.join(self.output_dir, thermal_props_filename)
+        with open(thermal_props_filepath, "w") as f:
+            f.write(
+                "Temperature (K)\tFree Energy (kJ/mol)\tEntropy (J/K*mol)\tHeat Capacity (J/K*mol)\n"
+            )
+            for i in range(len(tprop_dict["temperatures"])):
+                f.write(
+                    f"{tprop_dict['temperatures'][i]}\t{tprop_dict['free_energy'][i]}\t"
+                    f"{tprop_dict['entropy'][i]}\t{tprop_dict['heat_capacity'][i]}\n"
+                )
+        self.log(f"Thermal properties written to {thermal_props_filepath}")
+
+        # Calculate zero-point energy (ZPE)
+        zpe = tprop_dict["free_energy"][0] * 0.0103643  # Converting from kJ/mol to eV
+        self.log(f"Zero-point energy: {zpe} eV")
+
+        # Save to job info
+        self.job_info["phonopy_bands"] = phonopy_bands_figname
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return phonon, zpe
+
+    def analyze_defects(self):
+        """Analyze defects by generating, relaxing, and calculating vacancy formation energy."""
+        self.log("Starting defect analysis...")
+        generate_settings = self.defect_settings.get('generate_settings', {})
+        on_conventional_cell = generate_settings.get('on_conventional_cell', True)
+        enforce_c_size = generate_settings.get('enforce_c_size', 8)
+        extend = generate_settings.get('extend', 1)
+    # Generate defect structures from the original atoms
+        defect_structures = Vacancy(self.atoms).generate_defects(on_conventional_cell=on_conventional_cell, enforce_c_size=enforce_c_size, extend=extend)
+
+        for defect in defect_structures:
+        # Extract the defect structure and related metadata
+            defect_structure = Atoms.from_dict(defect.to_dict()["defect_structure"])
+        
+        # Construct a consistent defect name without Wyckoff notation
+            element = defect.to_dict()['symbol']
+            defect_name = f"{self.jid}_{element}"  # Consistent format
+            self.log(f"Analyzing defect: {defect_name}")
+
+        # Relax the defect structure
+            relaxed_defect_atoms = self.relax_defect_structure(defect_structure, name=defect_name)
+
+            if relaxed_defect_atoms is None:
+                self.log(f"Skipping {defect_name} due to failed relaxation.")
+                continue
+
+        # Retrieve energies for calculating the vacancy formation energy
+            vacancy_energy = self.job_info.get(f"final_energy_defect for {defect_name}")
+            bulk_energy = (
+                self.job_info.get("equilibrium_energy")
+                / self.atoms.num_atoms
+                * (defect_structure.num_atoms + 1)
+            )
+
+            if vacancy_energy is None or bulk_energy is None:
+                self.log(f"Skipping {defect_name} due to missing energy values.")
+                continue
+
+        # Get chemical potential and calculate vacancy formation energy
+            chemical_potential = self.get_chemical_potential(element)
+
+            if chemical_potential is None:
+                self.log(f"Skipping {defect_name} due to missing chemical potential for {element}.")
+                continue
+
+            vacancy_formation_energy = vacancy_energy - bulk_energy + chemical_potential
+
+        # Log and store the vacancy formation energy consistently
+            self.job_info[f"vacancy_formation_energy for {defect_name}"] = vacancy_formation_energy
+            self.log(f"Vacancy formation energy for {defect_name}: {vacancy_formation_energy} eV")
+
+    # Save the job info to a JSON file
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+        self.log("Defect analysis completed.")
+
+    def relax_defect_structure(self, atoms, name):
+        """Relax the defect structure and log the process."""
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.defect_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.defect_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log the final energy and relaxation status
+        self.log(
+            f"Final energy of FIRE optimization for defect structure: {final_energy}"
+        )
+        self.log(
+            f"Defect relaxation {'converged' if converged else 'did not converge'} within 200 steps."
+        )
+
+        # Update job info with the final energy and convergence status
+        self.job_info[f"final_energy_defect for {name}"] = final_energy
+        self.job_info[f"converged for {name}"] = converged
+
+        if converged:
+            poscar_filename = os.path.join(self.output_dir, f"POSCAR_{name}_relaxed.vasp")
+            poscar_defect = Poscar(relaxed_atoms)
+            poscar_defect.write_file(poscar_filename)
+            self.log(f"Relaxed defect structure saved to {poscar_filename}")
+
+        return relaxed_atoms if converged else None
+
+    def analyze_surfaces(self):
+        """
+        Perform surface analysis by generating surface structures, relaxing them, and calculating surface energies.
+        """
+        self.log(f"Analyzing surfaces for {self.jid}")
+
+        indices_list = self.surface_settings.get('indices_list', [
+            [1, 0, 0],
+            [1, 1, 1],
+            [1, 1, 0],
+            [0, 1, 1],
+            [0, 0, 1],
+            [0, 1, 0],
+        ])
+        layers = self.surface_settings.get('layers', 4)
+        vacuum = self.surface_settings.get('vacuum', 18)
+
+        for indices in indices_list:
+            # Generate surface and check for polarity
+            surface = (
+                Surface(atoms=self.atoms, indices=indices, layers=layers, vacuum=vacuum)
+                .make_surface()
+                .center_around_origin()
+            )
+            if surface.check_polar:
+                self.log(
+                    f"Skipping polar surface for {self.jid} with indices {indices}"
+                )
+                continue
+
+            # Write initial POSCAR for surface
+            poscar_surface = Poscar(atoms=surface)
+            poscar_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}.vasp",
+                )
+            )
+
+            # Relax the surface structure
+            relaxed_surface_atoms, final_energy = self.relax_surface_structure(
+                surface, indices
+            )
+
+            # If relaxation failed, skip further calculations
+            if relaxed_surface_atoms is None:
+                self.log(f"Skipping surface {indices} due to failed relaxation.")
+                continue
+
+            # Write relaxed POSCAR for surface
+            pos_relaxed_surface = Poscar(relaxed_surface_atoms)
+            pos_relaxed_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}_relaxed.vasp",
+                )
+            )
+
+            # Calculate and log surface energy
+            bulk_energy = self.job_info.get("equilibrium_energy")
+            if final_energy is None or bulk_energy is None:
+                self.log(
+                    f"Skipping surface energy calculation for {self.jid} with indices {indices} due to missing energy values."
+                )
+                continue
+
+            surface_energy = self.calculate_surface_energy(
+                final_energy, bulk_energy, relaxed_surface_atoms, surface
+            )
+
+            # Store the surface energy with the new naming convention
+            surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+            self.job_info[surface_name] = surface_energy
+            self.log(
+                f"Surface energy for {self.jid} with indices {indices}: {surface_energy} J/m^2"
+            )
+
+        # Save updated job info
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log("Surface analysis completed.")
+
+    def relax_surface_structure(self, atoms, indices):
+        """
+        Relax the surface structure and log the process.
+        """
+        filter_type = self.surface_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.surface_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        self.log(f"Starting surface relaxation for {self.jid} with indices {indices}")
+        start_time = time.time()
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+        # Implement other filters if needed
+            pass
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log relaxation results
+        self.log(
+            f"Final energy of FIRE optimization for surface structure: {final_energy}"
+        )
+        self.log(
+            f"Surface relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        end_time = time.time()
+        self.log(
+            f"Surface Relaxation Calculation time: {end_time - start_time} seconds"
+        )
+
+        # Update job info and return relaxed atoms if converged, otherwise return None
+        self.job_info[f"final_energy_surface_{indices}"] = final_energy
+        self.job_info[f"converged_surface_{indices}"] = converged
+
+        # Return both relaxed atoms and the final energy as a tuple
+        return (relaxed_atoms if converged else None), final_energy
+
+    def calculate_surface_energy(
+        self, final_energy, bulk_energy, relaxed_atoms, surface
+    ):
+        """
+        Calculate the surface energy based on the final energy of the relaxed surface and bulk energy.
+        """
+        # Calculate the number of bulk units in the surface supercell
+        num_units = surface.num_atoms / self.atoms.num_atoms
+
+        # Calculate the surface area using the lattice vectors
+        m = relaxed_atoms.lattice.matrix
+        surface_area = np.linalg.norm(np.cross(m[0], m[1]))
+
+        # Calculate surface energy in J/m^2
+        surface_energy = (
+            (final_energy - bulk_energy * num_units) * 16.02176565 / (2 * surface_area)
+        )
+
+        return surface_energy
+
+    def run_phonon3_analysis(self, relaxed_atoms):
+        from phono3py import Phono3py
+        """Run Phono3py analysis, process results, and generate thermal conductivity data."""
+        self.log(f"Starting Phono3py analysis for {self.jid}")
+
+        # Set parameters for the Phono3py calculation
+        dim = self.phonon3_settings.get('dim', [2, 2, 2])
+        distance = self.phonon3_settings.get('distance', 0.2)
+
+        #force_multiplier = 16
+
+        # Convert atoms to Phonopy-compatible object and set up Phono3py
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        bulk = relaxed_atoms.phonopy_converter()
+
+        phonon = Phono3py(bulk, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]])
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for scell in supercells:
+            ase_atoms = AseAtoms(
+                symbols=scell.get_chemical_symbols(),
+                scaled_positions=scell.get_scaled_positions(),
+                cell=scell.get_cell(),
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+            set_of_forces.append(forces)
+
+        # Set the forces and produce third-order force constants
+        forces = np.array(set_of_forces).reshape(-1, len(phonon.supercell), 3)
+        phonon.forces = forces
+        phonon.produce_fc3()
+
+        # Run thermal conductivity calculation
+        phonon.mesh_numbers = 30
+        phonon.init_phph_interaction()
+        phonon.run_thermal_conductivity(
+            temperatures=range(0, 1001, 10), write_kappa=True
+        )
+
+        # Collect thermal conductivity data
+        kappa = phonon.thermal_conductivity.kappa
+        self.log(f"Thermal conductivity: {kappa}")
+
+        # Move generated HDF5 files to the output directory
+        hdf5_file_pattern = "kappa-*.hdf5"
+        for hdf5_file in glob.glob(hdf5_file_pattern):
+            shutil.move(hdf5_file, os.path.join(self.output_dir, hdf5_file))
+
+        # Process Phono3py results and save plots
+        self.process_phonon3_results()
+
+        # Save updated job info to JSON
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(f"Phono3py analysis completed for {self.jid}")
+
+    def process_phonon3_results(self):
+        """Process Phono3py results and generate plots of thermal conductivity."""
+        file_pattern = os.path.join(self.output_dir, "kappa-*.hdf5")
+        file_list = glob.glob(file_pattern)
+
+        temperatures = np.arange(10, 101, 10)
+        kappa_xx_values = []
+
+        if file_list:
+            hdf5_filename = file_list[0]
+            self.log(f"Processing file: {hdf5_filename}")
+
+            for temperature_index in temperatures:
+                converted_kappa = self.convert_kappa_units(
+                    hdf5_filename, temperature_index
+                )
+                kappa_xx = converted_kappa[0]
+                kappa_xx_values.append(kappa_xx)
+                self.log(
+                    f"Temperature index {temperature_index}, converted kappa: {kappa_xx}"
+                )
+
+            # Save results to job_info
+            self.job_info["temperatures"] = temperatures.tolist()
+            self.job_info["kappa_xx_values"] = kappa_xx_values
+
+            # Plot temperature vs. converted kappa (xx element)
+            plt.figure(figsize=(8, 6))
+            plt.plot(
+                temperatures * 10, kappa_xx_values, marker="o", linestyle="-", color="b"
+            )
+            plt.xlabel("Temperature (K)")
+            plt.ylabel("Converted Kappa (xx element)")
+            plt.title("Temperature vs. Converted Kappa (xx element)")
+            plt.grid(True)
+            plt.savefig(
+                os.path.join(self.output_dir, "Temperature_vs_Converted_Kappa.png")
+            )
+            plt.close()
+        else:
+            self.log("No files matching the pattern were found.")
+
+    def convert_kappa_units(self, hdf5_filename, temperature_index):
+        """Convert thermal conductivity kappa from HDF5 file units."""
+        with h5py.File(hdf5_filename, "r") as f:
+            kappa_unit_conversion = f["kappa_unit_conversion"][()]
+            heat_capacity = f["heat_capacity"][:]
+            gv_by_gv = f["gv_by_gv"][:]
+            gamma = f["gamma"][:]
+
+            converted_kappa = (
+                kappa_unit_conversion
+                * heat_capacity[temperature_index, 2, 0]
+                * gv_by_gv[2, 0]
+                / (2 * gamma[temperature_index, 2, 0])
+            )
+
+            return converted_kappa
+
+    def calculate_thermal_expansion(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Calculate the thermal expansion coefficient using QHA."""
+
+        def log(message):
+            with open(self.log_file, "a") as f:
+                f.write(message + "\n")
+            print(message)
+
+        log("Starting thermal expansion analysis...")
+
+        # Step 1: Calculate finer E-V curve
+        vol, y, strained_structures, eos, kv, e0, v0 = self.fine_ev_curve(
+            atoms=relaxed_atoms, dx=np.linspace(-0.05, 0.05, 50)  # Denser grid
+        )
+
+        # Log Bulk modulus, equilibrium energy, and volume
+        log(
+            f"Bulk modulus: {kv} GPa, Equilibrium energy: {y[0]} eV, Volume: {vol[0]} ų"
+        )
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = y[0]
+        self.job_info["equilibrium_volume"] = vol[0]
+
+        # Step 2: Generate phonons for strained structures
+        free_energies, heat_capacities, entropies, temperatures = (
+            self.generate_phonons_for_volumes(
+                strained_structures,
+                calculator=self.calculator,
+                dim=[2, 2, 2],
+                distance=0.2,
+                mesh=[20, 20, 20],
+            )
+        )
+
+        # Step 3: Perform QHA-based thermal expansion analysis
+        alpha = self.perform_qha(
+            volumes=vol,
+            energies=y,
+            free_energies=free_energies,
+            heat_capacities=heat_capacities,
+            entropies=entropies,
+            temperatures=temperatures,
+            output_dir=self.output_dir,
+        )
+
+        self.log(f"Thermal expansion coefficient calculated: {alpha}")
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(
+            f"Thermal expansion analysis information saved to file: {self.jid}_{self.calculator_type}_job_info.json"
+        )
+
+    # Helper Functions Inside the Class
+    def fine_ev_curve(self, atoms, dx=np.linspace(-0.05, 0.05, 50)):
+        """
+        Generate a finer energy-volume curve for strained structures.
+        """
+        y = []
+        vol = []
+        strained_structures = []
+
+        for i in dx:
+            # Apply strain and get strained atoms
+            strained_atoms = atoms.strain_atoms(i)
+            ase_atoms = strained_atoms.ase_converter()  # Convert to ASE Atoms
+            ase_atoms.calc = self.calculator  # Assign the calculator
+
+            # Get potential energy and volume
+            energy = ase_atoms.get_potential_energy()
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+            strained_structures.append(strained_atoms)  # Save the strained structure
+
+        vol = np.array(vol)
+        y = np.array(y)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        eos = EquationOfState(vol, y, eos="murnaghan")
+        v0, e0, B = eos.fit()
+        kv = B / kJ * 1.0e24  # Convert to GPa
+
+        # Log important results
+        self.log(f"Bulk modulus: {kv} GPa")
+        self.log(f"Equilibrium energy: {e0} eV")
+        self.log(f"Equilibrium volume: {v0} ų")
+
+        # Save E-V curve plot
+        fig = plt.figure()
+        eos.plot()
+        ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+        fig.savefig(ev_plot_filename)
+        plt.close(fig)
+        self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+        # Save E-V curve data to a text file
+        ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+        with open(ev_data_filename, "w") as f:
+            f.write("Volume (ų)\tEnergy (eV)\n")
+            for v, e in zip(vol, y):
+                f.write(f"{v}\t{e}\n")
+        self.log(f"E-V curve data saved to {ev_data_filename}")
+
+        # Update job info with the results
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = e0
+        self.job_info["equilibrium_volume"] = v0
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+    def generate_phonons_for_volumes(
+        self, structures, calculator, dim=[2, 2, 2], distance=0.2, mesh=[20, 20, 20]
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        all_free_energies = []
+        all_heat_capacities = []
+        all_entropies = []
+        temperatures = np.arange(0, 300, 6)  # Define temperature range
+
+        for structure in structures:
+            # Convert structure to PhonopyAtoms
+            phonopy_atoms = PhonopyAtoms(
+                symbols=[str(e) for e in structure.elements],
+                positions=structure.cart_coords,
+                cell=structure.lattice.matrix,
+            )
+
+            # Initialize Phonopy object
+            phonon = Phonopy(
+                phonopy_atoms, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]]
+            )
+            phonon.generate_displacements(distance=distance)
+
+            # Calculate forces for displaced structures
+            supercells = phonon.get_supercells_with_displacements()
+            forces = []
+            for scell in supercells:
+                ase_atoms = AseAtoms(
+                    symbols=scell.symbols,
+                    positions=scell.positions,
+                    cell=scell.cell,
+                    pbc=True,
+                )
+                ase_atoms.calc = calculator
+                forces.append(ase_atoms.get_forces())
+
+            phonon.produce_force_constants(forces=forces)
+
+            # Post-processing to get thermal properties
+            phonon.run_mesh(mesh=mesh)
+            phonon.run_thermal_properties(t_min=0, t_step=6, t_max=294)
+            tprop_dict = phonon.get_thermal_properties_dict()
+
+            free_energies = tprop_dict["free_energy"]
+            heat_capacities = tprop_dict["heat_capacity"]
+            entropies = tprop_dict["entropy"]
+
+            all_entropies.append(entropies)
+            all_free_energies.append(free_energies)
+            all_heat_capacities.append(heat_capacities)
+
+        return (
+            np.array(all_free_energies),
+            np.array(all_heat_capacities),
+            np.array(all_entropies),
+            temperatures,
+        )
+
+    def perform_qha(
+        self,
+        volumes,
+        energies,
+        free_energies,
+        heat_capacities,
+        entropies,
+        temperatures,
+        output_dir,
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        # Debugging: print array sizes
+        print(f"Number of temperatures: {len(temperatures)}")
+        print(f"Number of free energy data points: {free_energies.shape}")
+        print(f"Number of volume data points: {len(volumes)}")
+
+        # Ensure that volumes, free_energies, and temperatures are consistent
+        if len(volumes) != len(temperatures):
+            raise ValueError(
+                "The number of volumes must match the number of temperatures"
+            )
+
+        # Initialize the QHA object
+        try:
+            qha = PhonopyQHA(
+                volumes=volumes,
+                electronic_energies=energies,
+                free_energy=free_energies,
+                cv=heat_capacities,
+                entropy=entropies,
+                temperatures=temperatures,
+                eos="murnaghan",  # or another EOS if needed
+                verbose=True,
+            )
+        except IndexError as e:
+            print(f"Error in QHA initialization: {e}")
+            raise
+
+        # Calculate thermal expansion and save plots
+        thermal_expansion_plot = os.path.join(output_dir, "thermal_expansion.png")
+        volume_temperature_plot = os.path.join(output_dir, "volume_temperature.png")
+        helmholtz_volume_plot = os.path.join(output_dir, "helmholtz_volume.png")
+
+        qha.get_thermal_expansion()
+
+        # Save thermal expansion plot
+        qha.plot_thermal_expansion()
+        plt.savefig(thermal_expansion_plot)
+
+        # Save volume-temperature plot
+        qha.plot_volume_temperature()
+        plt.savefig(volume_temperature_plot)
+
+        # Save Helmholtz free energy vs. volume plot
+        qha.plot_helmholtz_volume()
+        plt.savefig(helmholtz_volume_plot)
+
+        # Optionally save thermal expansion coefficient to a file
+        thermal_expansion_file = os.path.join(output_dir, "thermal_expansion.txt")
+        alpha = qha.write_thermal_expansion(filename=thermal_expansion_file)
+
+        return alpha
+
+    def general_melter(self, relaxed_atoms):
+        """Perform MD simulation to melt the structure, then quench it back to room temperature."""
+        self.log(f"Starting MD melting and quenching simulation for {self.jid}")
+
+        calculator = self.setup_calculator()
+        ase_atoms = relaxed_atoms.ase_converter()
+        dim = self.ensure_cell_size(ase_atoms, min_size=self.md_settings.get('min_size', 10.0))
+        supercell = relaxed_atoms.make_supercell_matrix(dim)
+        ase_atoms = supercell.ase_converter()
+        ase_atoms.calc = calculator
+
+        dt = self.md_settings.get('dt', 1) * ase.units.fs
+        temp0 = self.md_settings.get('temp0', 3500)
+        nsteps0 = self.md_settings.get('nsteps0', 1000)
+        temp1 = self.md_settings.get('temp1', 300)
+        nsteps1 = self.md_settings.get('nsteps1', 2000)
+        taut = self.md_settings.get('taut', 20) * ase.units.fs
+        trj = os.path.join(self.output_dir, f"{self.jid}_melt.traj")
+
+        # Initialize velocities and run the first part of the MD simulation
+        from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+        from ase.md.nvtberendsen import NVTBerendsen
+
+        MaxwellBoltzmannDistribution(ase_atoms, temp0 * ase.units.kB)
+        dyn = NVTBerendsen(ase_atoms, dt, temp0, taut=taut, trajectory=trj)
+
+        def myprint():
+            message = f"time={dyn.get_time() / ase.units.fs: 5.0f} fs T={ase_atoms.get_temperature(): 3.0f} K"
+            self.log(message)
+
+        dyn.attach(myprint, interval=20)
+        dyn.run(nsteps0)
+
+        # Cool down to room temperature
+        dyn.set_temperature(temp1)
+        dyn.run(nsteps1)
+
+        # Convert back to JARVIS atoms and save the final structure
+        final_atoms = ase_to_atoms(ase_atoms)
+        poscar_filename = os.path.join(
+            self.output_dir, f"POSCAR_{self.jid}_quenched_{self.calculator_type}.vasp"
+        )
+        from ase.io import write
+
+        write(poscar_filename, final_atoms.ase_converter(), format="vasp")
+        self.log(f"MD simulation completed. Final structure saved to {poscar_filename}")
+        self.job_info["quenched_atoms"] = final_atoms.to_dict()
+
+        return final_atoms
+
+    def calculate_rdf(self, quenched_atoms):
+        """Calculate Radial Distribution Function (RDF) for quenched structure and save plot."""
+        self.log(f"Starting RDF calculation for {self.jid}")
+        ase_atoms = quenched_atoms.ase_converter()
+        rmax = 3.5
+        nbins = 200
+
+        def perform_rdf_calculation(rmax):
+            from ase.ga.utilities import get_rdf
+
+            rdfs, distances = get_rdf(ase_atoms, rmax, nbins)
+            plt.figure()
+            plt.plot(distances, rdfs)
+            plt.xlabel("Distance (Å)")
+            plt.ylabel("RDF")
+            plt.title(
+                f"Radial Distribution Function for {self.jid} ({self.calculator_type})"
+            )
+            rdf_plot_filename = os.path.join(
+                self.output_dir, f"RDF_{self.jid}_{self.calculator_type}.png"
+            )
+            plt.savefig(rdf_plot_filename)
+            plt.close()
+            self.job_info["rdf_plot"] = rdf_plot_filename
+            self.log(f"RDF plot saved to {rdf_plot_filename}")
+            return rdf_plot_filename
+
+        try:
+            perform_rdf_calculation(rmax)
+        except ValueError as e:
+            if "The cell is not large enough" in str(e):
+                recommended_rmax = float(str(e).split("<")[1].split("=")[1])
+                self.log(f"Error: {e}. Adjusting rmax to {recommended_rmax}.")
+                perform_rdf_calculation(recommended_rmax)
+            else:
+                self.log(f"Error: {e}")
+                raise
+
+    def ensure_cell_size(self, ase_atoms, min_size):
+        """Ensure that all cell dimensions are at least min_size."""
+        cell_lengths = ase_atoms.get_cell().lengths()
+        scale_factors = np.ceil(min_size / cell_lengths).astype(int)
+        supercell_dims = [max(1, scale) for scale in scale_factors]
+        return supercell_dims
+
+    def analyze_interfaces(self):
+        """Perform interface analysis using intermat package."""
+        if not self.film_jid or not self.substrate_jid:
+            self.log("Film JID or substrate JID not provided, skipping interface analysis.")
+            return
+
+        self.log(f"Starting interface analysis between {self.film_jid} and {self.substrate_jid}")
+
+        # Ensure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # Prepare config
+        config = {
+            "film_jid": self.film_jid,
+            "substrate_jid": self.substrate_jid,
+            "film_index": self.film_index,
+            "substrate_index": self.substrate_index,
+            "disp_intvl": 0.05,
+            "calculator_method": self.calculator_type.lower(),
+        }
+
+        config_filename = os.path.join(
+            self.output_dir,
+            f"config_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.json"
+        )
+
+        # Save config file
+        save_dict_to_json(config, config_filename)
+        self.log(f"Config file created: {config_filename}")
+
+        # Run intermat script using subprocess in self.output_dir
+        command = f"run_intermat.py --config_file {os.path.basename(config_filename)}"
+        self.log(f"Running command: {command}")
+
+        try:
+            result = subprocess.run(
+                command,
+                shell=True,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=self.output_dir  # Set the working directory for the subprocess
+            )
+            self.log(f"Command output: {result.stdout}")
+        except subprocess.CalledProcessError as e:
+            self.log(f"Command failed with error: {e.stderr}")
+            return
+
+        # After execution, check for outputs in self.output_dir
+        main_results_filename = os.path.join(self.output_dir, "intermat_results.json")
+        if not os.path.exists(main_results_filename):
+            self.log(f"Results file not found: {main_results_filename}")
+            return
+
+        res = load_dict_from_json(main_results_filename)
+        w_adhesion = res.get("wads", [])
+        systems_info = res.get("systems", {})
+
+        # Handle intmat.png
+        intmat_filename = os.path.join(self.output_dir, "intmat.png")
+        if os.path.exists(intmat_filename):
+            new_intmat_filename = os.path.join(
+                self.output_dir,
+                f"intmat_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.png"
+            )
+            os.rename(intmat_filename, new_intmat_filename)
+            self.job_info["intmat_plot"] = new_intmat_filename
+            self.log(f"intmat.png saved as {new_intmat_filename}")
+        else:
+            self.log("intmat.png not found.")
+
+        if "wads" in res:
+            # Save additional plots or data as needed
+            self.job_info["interface_scan_results"] = main_results_filename
+            self.job_info["w_adhesion"] = w_adhesion
+            self.job_info["systems_info"] = systems_info
+            self.log(f"Interface scan results saved to {main_results_filename}")
+            self.log(f"w_adhesion: {w_adhesion}")
+            self.log(f"systems_info: {systems_info}")
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+        else:
+            self.log(f"No 'wads' key in results file: {main_results_filename}")
+
+    def get_job_info_filename(self):
+        if hasattr(self, 'jid') and self.jid:
+            return os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            )
+        else:
+            return os.path.join(
+                self.output_dir, f"Interface_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}_job_info.json"
+            )
+
+    import numpy as np
+    import pandas as pd
+    from sklearn.metrics import mean_absolute_error, r2_score
+    import matplotlib.pyplot as plt
+    from matplotlib.gridspec import GridSpec
+    import os
+
+    def run_all(self):
+        """Run selected analyses based on configuration."""
+        # Start timing the entire run
+        start_time = time.time()
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for analysis.")
+            self.atoms = self.atoms.get_conventional_atoms
+        else:
+            self.atoms = self.atoms
+        # Relax the structure if specified
+        if 'relax_structure' in self.properties_to_calculate:
+            relaxed_atoms = self.relax_structure()
+        else:
+            relaxed_atoms = self.atoms
+
+        # Proceed only if the structure is relaxed or original atoms are used
+        if relaxed_atoms is None:
+            self.log("Relaxation did not converge. Exiting.")
+            return
+
+        if 'calculate_forces' in self.properties_to_calculate:
+            self.calculate_forces(self.atoms)
+
+        # Lattice parameters before and after relaxation
+        lattice_initial = self.atoms.lattice
+        lattice_final = relaxed_atoms.lattice
+
+        # Prepare final results dictionary
+        final_results = {}
+
+        # Initialize variables for error calculation
+        err_a = err_b = err_c = err_vol = err_form = err_kv = err_c11 = err_c44 = err_surf_en = err_vac_en = np.nan
+        form_en_entry = kv_entry = c11_entry = c44_entry = 0
+
+        # Calculate E-V curve and bulk modulus if specified
+        if 'calculate_ev_curve' in self.properties_to_calculate:
+            _, _, _, _, bulk_modulus, _, _ = self.calculate_ev_curve(relaxed_atoms)
+            kv_entry = self.reference_data.get("bulk_modulus_kv", 0)
+            final_results['modulus'] = {
+                'kv': bulk_modulus,
+                'kv_entry': kv_entry
+            }
+            err_kv = mean_absolute_error([kv_entry], [bulk_modulus]) if bulk_modulus is not None else np.nan
+
+                
+        # Formation energy
+        if 'calculate_formation_energy' in self.properties_to_calculate:
+            formation_energy = self.calculate_formation_energy(relaxed_atoms)
+            form_en_entry = self.reference_data.get("formation_energy_peratom", 0)
+            final_results['form_en'] = {
+                'form_energy': formation_energy,
+                'form_energy_entry': form_en_entry
+            }
+            err_form = mean_absolute_error([form_en_entry], [formation_energy])
+
+        # Elastic tensor
+        if 'calculate_elastic_tensor' in self.properties_to_calculate:
+            elastic_tensor = self.calculate_elastic_tensor(relaxed_atoms)
+            c11_entry = self.reference_data.get("elastic_tensor", [[0]])[0][0]
+            c44_entry = self.reference_data.get("elastic_tensor", [[0, 0, 0, [0, 0, 0, 0]]])[3][3]
+            final_results['elastic_tensor'] = {
+                'c11': elastic_tensor.get("C_11", 0),
+                'c44': elastic_tensor.get("C_44", 0),
+                'c11_entry': c11_entry,
+                'c44_entry': c44_entry
+            }
+            err_c11 = mean_absolute_error([c11_entry], [elastic_tensor.get("C_11", np.nan)])
+            err_c44 = mean_absolute_error([c44_entry], [elastic_tensor.get("C_44", np.nan)])
+
+        # Phonon analysis
+        if 'run_phonon_analysis' in self.properties_to_calculate:
+            phonon, zpe = self.run_phonon_analysis(relaxed_atoms)
+            final_results['zpe'] = zpe
+        else:
+            zpe = None
+
+        # Surface energy analysis
+        if 'analyze_surfaces' in self.properties_to_calculate:
+            self.analyze_surfaces()
+            surf_en, surf_en_entry = [], []
+            surface_entries = get_surface_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+
+            indices_list = self.surface_settings.get('indices_list', [
+                [1, 0, 0],
+                [1, 1, 1],
+                [1, 1, 0],
+                [0, 1, 1],
+                [0, 0, 1],
+                [0, 1, 0],
+            ])
+
+            for indices in indices_list:
+                surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+                calculated_surface_energy = self.job_info.get(surface_name, 0)
+                try:
+                    # Try to match the surface entry
+                    matching_entry = next(
+                        (entry for entry in surface_entries if entry['name'].strip() == surface_name.strip()),
+                        None
+                    )
+                    if matching_entry and calculated_surface_energy != 0 and matching_entry["surf_en_entry"] != 0:
+                        surf_en.append(calculated_surface_energy)
+                        surf_en_entry.append(matching_entry["surf_en_entry"])
+                    else:
+                        print(f"No valid matching entry found for {surface_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing surface {surface_name}: {e}")
+                    self.log(f"Error processing surface {surface_name}: {str(e)}")
+                    continue  # Skip this surface and move to the next one
+            final_results['surface_energy'] = [
+                {
+                    "name": f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}",
+                    "surf_en": se,
+                    "surf_en_entry": see
+                }
+                for se, see, indices in zip(surf_en, surf_en_entry, indices_list)
+            ]
+            err_surf_en = mean_absolute_error(surf_en_entry, surf_en) if surf_en else np.nan
+
+        # Vacancy energy analysis
+        if 'analyze_defects' in self.properties_to_calculate:
+            self.analyze_defects()
+            vac_en, vac_en_entry = [], []
+            vacancy_entries = get_vacancy_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+            for defect in Vacancy(self.atoms).generate_defects(on_conventional_cell=True, enforce_c_size=8, extend=1):
+                defect_name = f"{self.jid}_{defect.to_dict()['symbol']}"
+                vacancy_energy = self.job_info.get(f"vacancy_formation_energy for {defect_name}", 0)
+                try:
+                    # Try to match the vacancy entry
+                    matching_entry = next(
+                        (entry for entry in vacancy_entries if entry['symbol'] == defect_name),
+                        None
+                    )
+                    if matching_entry and vacancy_energy != 0 and matching_entry['vac_en_entry'] != 0:
+                        vac_en.append(vacancy_energy)
+                        vac_en_entry.append(matching_entry['vac_en_entry'])
+                    else:
+                        print(f"No valid matching entry found for {defect_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing defect {defect_name}: {e}")
+                    self.log(f"Error processing defect {defect_name}: {str(e)}")
+                    continue  # Skip this defect and move to the next one
+            final_results['vacancy_energy'] = [
+                {"name": ve_name, "vac_en": ve, "vac_en_entry": vee}
+                for ve_name, ve, vee in zip(
+                    [f"{self.jid}_{defect.to_dict()['symbol']}" for defect in Vacancy(self.atoms).generate_defects(
+                        on_conventional_cell=True, enforce_c_size=8, extend=1
+                    )],
+                    vac_en,
+                    vac_en_entry
+                )
+            ]
+            err_vac_en = mean_absolute_error(vac_en_entry, vac_en) if vac_en else np.nan
+
+        # Additional analyses
+        if 'analyze_interfaces' in self.properties_to_calculate and self.film_jid and self.substrate_jid:
+            self.analyze_interfaces()
+
+        if 'run_phonon3_analysis' in self.properties_to_calculate:
+            self.run_phonon3_analysis(relaxed_atoms)
+
+        if 'calculate_thermal_expansion' in self.properties_to_calculate:
+            self.calculate_thermal_expansion(relaxed_atoms)
+
+        if 'general_melter' in self.properties_to_calculate:
+            quenched_atoms = self.general_melter(relaxed_atoms)
+            if 'calculate_rdf' in self.properties_to_calculate:
+                self.calculate_rdf(quenched_atoms)
+
+        # Record lattice parameters
+        final_results['energy'] = {
+            "initial_a": lattice_initial.a,
+            "initial_b": lattice_initial.b,
+            "initial_c": lattice_initial.c,
+            "initial_vol": lattice_initial.volume,
+            "final_a": lattice_final.a,
+            "final_b": lattice_final.b,
+            "final_c": lattice_final.c,
+            "final_vol": lattice_final.volume,
+            "energy": self.job_info.get("final_energy_structure", 0)
+        }
+
+        # Error calculations
+        err_a = mean_absolute_error([lattice_initial.a], [lattice_final.a])
+        err_b = mean_absolute_error([lattice_initial.b], [lattice_final.b])
+        err_c = mean_absolute_error([lattice_initial.c], [lattice_final.c])
+        err_vol = mean_absolute_error([lattice_initial.volume], [lattice_final.volume])
+
+        # Create an error dictionary
+        error_dat = {
+            "err_a": err_a,
+            "err_b": err_b,
+            "err_c": err_c,
+            "err_form": err_form,
+            "err_vol": err_vol,
+            "err_kv": err_kv,
+            "err_c11": err_c11,
+            "err_c44": err_c44,
+            "err_surf_en": err_surf_en,
+            "err_vac_en": err_vac_en,
+            "time": time.time() - start_time
+        }
+
+        print("Error metrics calculated:", error_dat)
+
+        # Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+        # Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+        # Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        # Write results to a JSON file
+        output_file = os.path.join(self.output_dir, f"{self.jid}_{self.calculator_type}_results.json")
+        save_dict_to_json(final_results, output_file)
+
+        # Log total time
+        total_time = error_dat['time']
+        self.log(f"Total time for run: {total_time} seconds")
+
+        return error_dat
+
+
+# Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+# Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+# Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        return error_dat
+
+    def plot_error_scorecard(self, df):
+        import plotly.express as px
+
+        fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+        unique_dir = os.path.basename(self.output_dir)
+        fname_plot = os.path.join(self.output_dir, f"{unique_dir}_error_scorecard.png")
+        fig.write_image(fname_plot)
+        fig.show()
+
+def analyze_multiple_structures(jid_list, calculator_types, chemical_potentials_file):
+    composite_error_data = {}
+
+    for calculator_type in calculator_types:
+        # List to store individual error DataFrames
+        error_dfs = []
+
+        for jid in jid_list:
+            print(f"Analyzing {jid} with {calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                jid=jid,
+                calculator_type=calculator_type,
+                chemical_potentials_file=chemical_potentials_file,
+            )
+            # Run analysis and get error data
+            error_dat = analyzer.run_all()
+            error_df = pd.DataFrame([error_dat])
+            error_dfs.append(error_df)
+
+        # Concatenate all error DataFrames
+        all_errors_df = pd.concat(error_dfs, ignore_index=True)
+
+        # Compute composite errors by ignoring NaN values
+        composite_error = all_errors_df.mean(skipna=True).to_dict()
+
+        # Store the composite error data for this calculator type
+        composite_error_data[calculator_type] = composite_error
+
+    # Once all materials and calculators have been processed, create a DataFrame
+    composite_df = pd.DataFrame(composite_error_data).transpose()
+
+    # Plot the composite scorecard
+    plot_composite_scorecard(composite_df)
+
+    # Save the composite dataframe
+    composite_df.to_csv("composite_error_data.csv", index=True)
+
+def analyze_multiple_interfaces(film_jid_list, substrate_jid_list, calculator_types, chemical_potentials_file, film_index="1_1_0", substrate_index="1_1_0"):
+    for calculator_type in calculator_types:
+        for film_jid in film_jid_list:
+            for substrate_jid in substrate_jid_list:
+                print(f"Analyzing interface between {film_jid} and {substrate_jid} with {calculator_type}...")
+                analyzer = MaterialsAnalyzer(
+                    calculator_type=calculator_type,
+                    chemical_potentials_file=chemical_potentials_file,
+                    film_jid=film_jid,
+                    substrate_jid=substrate_jid,
+                    film_index=film_index,
+                    substrate_index=substrate_index,
+                )
+                analyzer.analyze_interfaces()
+
+def plot_composite_scorecard(df):
+    """Plot the composite scorecard for all calculators"""
+    fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+    fig.update_layout(title="Composite Scorecard for Calculators")
+    
+    # Save plot
+    fname_plot = "composite_error_scorecard.png"
+    fig.write_image(fname_plot)
+    fig.show()
+
+
+class MLearnForcesAnalyzer:
+    def __init__(self, calculator_type, mlearn_elements, output_dir=None):
+        self.calculator_type = calculator_type
+        self.mlearn_elements = mlearn_elements
+        elements_str = "_".join(self.mlearn_elements)
+        self.output_dir = output_dir or f"mlearn_analysis_{elements_str}_{calculator_type}"
+        os.makedirs(self.output_dir, exist_ok=True)
+        self.log_file = os.path.join(self.output_dir, "mlearn_analysis_log.txt")
+        self.setup_logger()
+        self.calculator = self.setup_calculator()
+        self.job_info = {
+            "calculator_type": calculator_type,
+            "mlearn_elements": mlearn_elements,
+        }
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger("MLearnForcesAnalyzer")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        self.logger.info(message)
+        print(message)
+
+    def setup_calculator(self):
+        return setup_calculator(self.calculator_type)
+
+    def run(self):
+        for element in self.mlearn_elements:
+            self.compare_mlearn_properties(element)
+
+    def compare_mlearn_properties(self, element):
+        """
+        Compare forces and stresses calculated by the FF calculator with mlearn DFT data for a given element.
+
+        Args:
+            element (str): Element symbol to filter structures (e.g., 'Si').
+        """
+        # Download the mlearn dataset if not already present
+        mlearn_zip_path = 'mlearn.json.zip'
+        if not os.path.isfile(mlearn_zip_path):
+            self.log("Downloading mlearn dataset...")
+            url = 'https://figshare.com/ndownloader/files/40357663'
+            response = requests.get(url)
+            with open(mlearn_zip_path, 'wb') as f:
+                f.write(response.content)
+            self.log("Download completed.")
+
+        # Read the JSON data from the zip file
+        with zipfile.ZipFile(mlearn_zip_path, 'r') as z:
+            with z.open('mlearn.json') as f:
+                mlearn_data = json.load(f)
+
+        # Convert mlearn data to DataFrame
+        df = pd.DataFrame(mlearn_data)
+
+        # Filter the dataset for the specified element
+        df['elements'] = df['atoms'].apply(lambda x: x['elements'])
+        df = df[df['elements'].apply(lambda x: element in x)]
+        df = df.reset_index(drop=True)
+        self.log(f"Filtered dataset to {len(df)} entries containing element '{element}'")
+
+        # Initialize lists to store results
+        force_results = []
+        stress_results = []
+
+        # Iterate over each structure
+        for idx, row in df.iterrows():
+            jid = row.get('jid', f"structure_{idx}")
+            atoms_dict = row['atoms']
+            atoms = Atoms.from_dict(atoms_dict)
+            dft_forces = np.array(row['forces'])
+            dft_stresses = np.array(row['stresses'])  # Original stresses in kBar
+
+            # Convert DFT stresses from kBar to GPa
+            dft_stresses_GPa = dft_stresses * -0.1  # kBar to GPa
+
+            # Convert DFT stresses to full 3x3 tensors
+            if dft_stresses_GPa.ndim == 1 and dft_stresses_GPa.size == 6:
+                dft_stress_tensor = voigt_6_to_full_3x3_stress(dft_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: DFT stresses not in expected format.")
+                continue  # Skip structures with unexpected stress format
+
+            # Calculate predicted properties
+            predicted_forces, predicted_stresses = self.calculate_properties(atoms)
+
+            # Convert predicted stresses from eV/ų to GPa
+            if predicted_stresses is not None and predicted_stresses.size == 6:
+                predicted_stresses_GPa = predicted_stresses * 160.21766208  # eV/ų to GPa
+                predicted_stress_tensor = voigt_6_to_full_3x3_stress(predicted_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: Predicted stresses not available.")
+                continue  # Skip structures where stresses are not available
+
+            # Flatten the 3x3 stress tensors to 9-component arrays for comparison
+            dft_stress_flat = dft_stress_tensor.flatten()
+            predicted_stress_flat = predicted_stress_tensor.flatten()
+
+            # Store the results
+            force_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_forces.flatten())),
+                'prediction': ';'.join(map(str, predicted_forces.flatten())),
+            })
+            stress_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_stress_flat)),
+                'prediction': ';'.join(map(str, predicted_stress_flat)),
+            })
+
+            # Optional: Progress indicator
+            if idx % 10 == 0:
+                self.log(f"Processed {idx + 1}/{len(df)} structures.")
+
+        # Ensure we have data to process
+        if not force_results or not stress_results:
+            self.log("No valid data found for forces or stresses. Exiting.")
+            return
+
+        # Save results to CSV files
+        force_df = pd.DataFrame(force_results)
+        force_csv = os.path.join(self.output_dir, f'AI-MLFF-forces-mlearn_{element}-test-multimae.csv')
+        force_df.to_csv(force_csv, index=False)
+        self.log(f"Saved force comparison data to '{force_csv}'")
+
+        stress_df = pd.DataFrame(stress_results)
+        stress_csv = os.path.join(self.output_dir, f'AI-MLFF-stresses-mlearn_{element}-test-multimae.csv')
+        stress_df.to_csv(stress_csv, index=False)
+        self.log(f"Saved stress comparison data to '{stress_csv}'")
+
+        # Zip the CSV files
+        self.zip_file(force_csv)
+        self.zip_file(stress_csv)
+
+        # Calculate error metrics
+        # Forces MAE
+        target_forces = np.concatenate(force_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_forces = np.concatenate(force_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        forces_mae = mean_absolute_error(target_forces, pred_forces)
+        self.log(f"Forces MAE for element '{element}': {forces_mae:.6f} eV/Å")
+
+        # Stresses MAE
+        target_stresses = np.concatenate(stress_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_stresses = np.concatenate(stress_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        stresses_mae = mean_absolute_error(target_stresses, pred_stresses)
+        self.log(f"Stresses MAE for element '{element}': {stresses_mae:.6f} GPa")
+
+        # Save MAE to job_info
+        self.job_info[f'forces_mae_{element}'] = forces_mae
+        self.job_info[f'stresses_mae_{element}'] = stresses_mae
+        self.save_job_info()
+
+        # Plot parity plots
+        forces_plot_filename = os.path.join(self.output_dir, f'forces_parity_plot_{element}.png')
+        self.plot_parity(target_forces, pred_forces, 'Forces', 'eV/Å', forces_plot_filename, element)
+
+        stresses_plot_filename = os.path.join(self.output_dir, f'stresses_parity_plot_{element}.png')
+        self.plot_parity(target_stresses, pred_stresses, 'Stresses', 'GPa', stresses_plot_filename, element)
+
+    def calculate_properties(self, atoms):
+        """
+        Calculate forces and stresses on the given atoms.
+
+        Returns:
+            Tuple of forces and stresses.
+        """
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Calculate properties
+        forces = ase_atoms.get_forces()
+        stresses = ase_atoms.get_stress()  # Voigt 6-component stress
+
+        return forces, stresses  # Return forces and stresses in Voigt notation
+
+    def plot_parity(self, target, prediction, property_name, units, filename, element):
+        """
+        Plot parity plot for a given property.
+
+        Args:
+            target (array-like): Target values.
+            prediction (array-like): Predicted values.
+            property_name (str): Name of the property (e.g., 'Forces').
+            units (str): Units of the property (e.g., 'eV/Å' or 'GPa').
+            filename (str): Filename to save the plot.
+            element (str): Element symbol.
+        """
+        plt.figure(figsize=(8, 8), dpi=300)
+        plt.scatter(target, prediction, alpha=0.5, edgecolors='k', s=20)
+        min_val = min(np.min(target), np.min(prediction))
+        max_val = max(np.max(target), np.max(prediction))
+        plt.plot([min_val, max_val], [min_val, max_val], 'k--', lw=2)
+        plt.xlabel(f'Target {property_name} ({units})', fontsize=14)
+        plt.ylabel(f'Predicted {property_name} ({units})', fontsize=14)
+        plt.title(f'Parity Plot for {property_name} - Element {element}', fontsize=16)
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(filename)
+        plt.close()
+        self.log(f"Saved parity plot for {property_name} as '{filename}'")
+
+    def zip_file(self, filename):
+        zip_filename = filename + '.zip'
+        with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
+            zf.write(filename, arcname=os.path.basename(filename))
+        os.remove(filename)  # Remove the original file
+        self.log(f"Zipped data to '{zip_filename}'")
+
+    def save_job_info(self):
+        job_info_filename = os.path.join(self.output_dir, f"mlearn_{self.calculator_type}_job_info.json")
+        with open(job_info_filename, 'w') as f:
+            json.dump(self.job_info, f, indent=4)
+
+
+#jid_list=['JVASP-1002']
+jid_list_all = [ 'JVASP-1002', 'JVASP-816', 'JVASP-867', 'JVASP-1029', 'JVASP-861','JVASP-30', 'JVASP-8169', 'JVASP-890', 'JVASP-8158','JVASP-8118',
+    'JVASP-107', 'JVASP-39', 'JVASP-7844', 'JVASP-35106', 'JVASP-1174',
+    'JVASP-1372', 'JVASP-91', 'JVASP-1186', 'JVASP-1408', 'JVASP-105410',
+    'JVASP-1177', 'JVASP-79204', 'JVASP-1393', 'JVASP-1312', 'JVASP-1327',
+    'JVASP-1183', 'JVASP-1192', 'JVASP-8003', 'JVASP-96', 'JVASP-1198',
+    'JVASP-1195', 'JVASP-9147', 'JVASP-41', 'JVASP-34674', 'JVASP-113',
+    'JVASP-32', 'JVASP-840', 'JVASP-21195', 'JVASP-981', 'JVASP-969',
+    'JVASP-802', 'JVASP-943', 'JVASP-14812', 'JVASP-984', 'JVASP-972',
+    'JVASP-958', 'JVASP-901', 'JVASP-1702', 'JVASP-931', 'JVASP-963',
+    'JVASP-95', 'JVASP-1201', 'JVASP-14837', 'JVASP-825', 'JVASP-966',
+    'JVASP-993', 'JVASP-23', 'JVASP-828', 'JVASP-1189', 'JVASP-810',
+    'JVASP-7630', 'JVASP-819', 'JVASP-1180', 'JVASP-837', 'JVASP-919',
+    'JVASP-7762', 'JVASP-934', 'JVASP-858', 'JVASP-895']
+#calculator_types = ["alignn_ff_aff307k_lmdb_param_low_rad_use_force_mult_mp_tak4","alignn_ff_v5.27.2024","alignn_ff_aff307k_kNN_2_2_128"]
+
+if __name__ == "__main__":
+    import pprint
+    parser = argparse.ArgumentParser(description="Run Materials Analyzer")
+    parser.add_argument("--input_file", default="input.json", type=str, help="Path to the input configuration JSON file")
+    args = parser.parse_args()
+
+    input_file = loadjson(args.input_file)
+    input_file_data = CHIPSFFConfig(**input_file)
+    pprint.pprint(input_file_data.dict())
+
+    # If film_id is provided, treat it as a list
+    film_jids = input_file_data.film_id if input_file_data.film_id else []
+
+    # If substrate_id is provided, treat it as a list
+    substrate_jids = input_file_data.substrate_id if input_file_data.substrate_id else []
+
+    # Case 1: Interface calculations with film_jid and substrate_jid
+    if film_jids and substrate_jids:
+        # Loop through all film and substrate JIDs and perform interface analysis
+        for film_jid, substrate_jid in zip(film_jids, substrate_jids):
+            print(f"Analyzing interface between {film_jid} and {substrate_jid} with {input_file_data.calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                calculator_type=input_file_data.calculator_type,
+                chemical_potentials_file=input_file_data.chemical_potentials_file,
+                film_jid=film_jid,
+                substrate_jid=substrate_jid,
+                film_index=input_file_data.film_index,
+                substrate_index=input_file_data.substrate_index,
+                bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+                phonon_settings=input_file_data.phonon_settings,
+                properties_to_calculate=input_file_data.properties_to_calculate,
+            )
+            analyzer.analyze_interfaces()
+
+    # Case 2: Single JID provided
+    elif input_file_data.jid and input_file_data.calculator_type:
+        print(f"Analyzing {input_file_data.jid} with {input_file_data.calculator_type}...")
+        analyzer = MaterialsAnalyzer(
+            jid=input_file_data.jid,
+            calculator_type=input_file_data.calculator_type,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+        analyzer.run_all()
+
+    # Case 3: Multiple JIDs and calculator types provided (batch processing)
+    elif input_file_data.jid_list and input_file_data.calculator_types:
+        analyze_multiple_structures(
+            jid_list=input_file_data.jid_list,
+            calculator_types=input_file_data.calculator_types,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+
+    elif input_file_data.mlearn_elements and input_file_data.calculator_type:
+        print(f"Running mlearn forces comparison for elements {input_file_data.mlearn_elements} with {input_file_data.calculator_type}...")
+        mlearn_analyzer = MLearnForcesAnalyzer(
+            calculator_type=input_file_data.calculator_type,
+            mlearn_elements=input_file_data.mlearn_elements
+        )
+        mlearn_analyzer.run()
+    else:
+        print("Please provide valid arguments in the configuration file.")
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/AI-MLFF-forces-mlearnall_Ni-test-multimae.csv.zip b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Ni-test-multimae.csv.zip
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/AI-MLFF-forces-mlearnall_Ni-test-multimae.csv.zip
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Ni-test-multimae.csv.zip
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/metadata.json
new file mode 100644
index 000000000..93f5c9fd0
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/metadata.json
@@ -0,0 +1,15 @@
+{
+    "model_name": "CHIPS-FF",
+    "project_url": "https://github.com/usnistgov/chipsff",
+    "date_submitted": "11-22-2024",
+    "author_email": "daniel.wines@nist.gov",
+    "time_taken_seconds": {},
+    "team_name": "JARVIS",
+    "language": "python",
+    "os": "linux",
+    "software_used": "jarvis-tools,numpy,scipy,torch,alignn",
+    "hardware_used": "mr-french at NIST",
+    "git_url": [
+        "https://github.com/usnistgov/chipsff"
+    ]
+}
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/mlearn_alignn_ff_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/mlearn_alignn_ff_job_info.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/mlearn_analysis_log.txt
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/mlearn_analysis_log.txt
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/mlearn_analysis_log.txt
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/run.sh
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/run.sh
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/run.sh
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/run_chipsff.py
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff/run_chipsff.py
rename to jarvis_leaderboard/contributions/mlearn_analysis_Ni_alignn_ff_5_27_24/run_chipsff.py
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HcmV?d00001

diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/metadata.json
new file mode 100644
index 000000000..93f5c9fd0
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/metadata.json
@@ -0,0 +1,15 @@
+{
+    "model_name": "CHIPS-FF",
+    "project_url": "https://github.com/usnistgov/chipsff",
+    "date_submitted": "11-22-2024",
+    "author_email": "daniel.wines@nist.gov",
+    "time_taken_seconds": {},
+    "team_name": "JARVIS",
+    "language": "python",
+    "os": "linux",
+    "software_used": "jarvis-tools,numpy,scipy,torch,alignn",
+    "hardware_used": "mr-french at NIST",
+    "git_url": [
+        "https://github.com/usnistgov/chipsff"
+    ]
+}
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
new file mode 100644
index 000000000..f0c9439d7
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/mlearn_alignn_ff_12_2_24_job_info.json
@@ -0,0 +1,8 @@
+{
+    "calculator_type": "alignn_ff_12_2_24",
+    "mlearn_elements": [
+        "Si"
+    ],
+    "forces_mae_Si": 0.3240733623289393,
+    "stresses_mae_Si": 1.1248588642008068
+}
\ No newline at end of file
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/mlearn_analysis_log.txt
new file mode 100644
index 000000000..1d6b74244
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/mlearn_analysis_log.txt
@@ -0,0 +1,33 @@
+2024-12-03 16:34:59,997 - INFO - Filtered dataset to 239 entries containing element 'Si'
+2024-12-03 16:35:07,045 - INFO - Processed 1/239 structures.
+2024-12-03 16:35:10,642 - INFO - Processed 11/239 structures.
+2024-12-03 16:35:14,163 - INFO - Processed 21/239 structures.
+2024-12-03 16:35:17,816 - INFO - Processed 31/239 structures.
+2024-12-03 16:35:21,642 - INFO - Processed 41/239 structures.
+2024-12-03 16:35:25,450 - INFO - Processed 51/239 structures.
+2024-12-03 16:35:28,378 - INFO - Processed 61/239 structures.
+2024-12-03 16:35:30,682 - INFO - Processed 71/239 structures.
+2024-12-03 16:35:34,128 - INFO - Processed 81/239 structures.
+2024-12-03 16:35:37,545 - INFO - Processed 91/239 structures.
+2024-12-03 16:35:40,656 - INFO - Processed 101/239 structures.
+2024-12-03 16:35:44,554 - INFO - Processed 111/239 structures.
+2024-12-03 16:35:48,984 - INFO - Processed 121/239 structures.
+2024-12-03 16:35:52,265 - INFO - Processed 131/239 structures.
+2024-12-03 16:35:55,071 - INFO - Processed 141/239 structures.
+2024-12-03 16:35:58,185 - INFO - Processed 151/239 structures.
+2024-12-03 16:36:01,610 - INFO - Processed 161/239 structures.
+2024-12-03 16:36:05,200 - INFO - Processed 171/239 structures.
+2024-12-03 16:36:09,335 - INFO - Processed 181/239 structures.
+2024-12-03 16:36:12,941 - INFO - Processed 191/239 structures.
+2024-12-03 16:36:17,017 - INFO - Processed 201/239 structures.
+2024-12-03 16:36:21,251 - INFO - Processed 211/239 structures.
+2024-12-03 16:36:25,079 - INFO - Processed 221/239 structures.
+2024-12-03 16:36:28,213 - INFO - Processed 231/239 structures.
+2024-12-03 16:36:31,204 - INFO - Saved force comparison data to 'mlearn_analysis_Si_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Si-test-multimae.csv'
+2024-12-03 16:36:31,212 - INFO - Saved stress comparison data to 'mlearn_analysis_Si_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Si-test-multimae.csv'
+2024-12-03 16:36:31,357 - INFO - Zipped data to 'mlearn_analysis_Si_alignn_ff_12_2_24/AI-MLFF-forces-mlearn_Si-test-multimae.csv.zip'
+2024-12-03 16:36:31,368 - INFO - Zipped data to 'mlearn_analysis_Si_alignn_ff_12_2_24/AI-MLFF-stresses-mlearn_Si-test-multimae.csv.zip'
+2024-12-03 16:36:31,404 - INFO - Forces MAE for element 'Si': 0.324073 eV/Å
+2024-12-03 16:36:31,410 - INFO - Stresses MAE for element 'Si': 1.124859 GPa
+2024-12-03 16:36:33,903 - INFO - Saved parity plot for Forces as 'mlearn_analysis_Si_alignn_ff_12_2_24/forces_parity_plot_Si.png'
+2024-12-03 16:36:34,889 - INFO - Saved parity plot for Stresses as 'mlearn_analysis_Si_alignn_ff_12_2_24/stresses_parity_plot_Si.png'
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/run.sh
new file mode 100644
index 000000000..0e331ca03
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/run.sh
@@ -0,0 +1,148 @@
+#!/bin/bash
+
+# Create logs directory if it doesn't exist
+mkdir -p logs
+
+# Define arrays of JIDs, calculators, and mlearn elements
+calculator_types=("alignn_ff_12_2_24")
+mlearn_elements=("Si" "Ge" "Cu" "Ni" "Li" "Mo")
+
+# Function to submit a job for bulk material calculations
+submit_bulk_job() {
+  local jid=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=30-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=${jid}_${calculator}
+#SBATCH --output=logs/${jid}_${calculator}_%j.out
+#SBATCH --error=logs/${jid}_${calculator}_%j.err
+
+# Generate input JSON file for this combination
+cat > input_${jid}_${calculator}.json <<JSON
+{
+  "jid": "$jid",
+  "calculator_type": "$calculator",
+  "chemical_potentials_file": "chemical_potentials.json",
+  "properties_to_calculate": [
+    "relax_structure",
+    "calculate_ev_curve",
+    "calculate_formation_energy",
+    "calculate_elastic_tensor",
+    "run_phonon_analysis",
+    "analyze_surfaces",
+    "analyze_defects",
+    "run_phonon3_analysis",
+    "general_melter",
+    "calculate_rdf"
+  ],
+  "bulk_relaxation_settings": {
+    "filter_type": "ExpCellFilter",
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": false
+    }
+  },
+  "phonon_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "use_conventional_cell": true,
+  "surface_settings": {
+    "indices_list": [
+      [0, 1, 0],
+      [0, 0, 1]
+    ],
+    "layers": 4,
+    "vacuum": 18,
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "defect_settings": {
+    "generate_settings": {
+      "on_conventional_cell": true,
+      "enforce_c_size": 8,
+      "extend": 1
+    },
+    "relaxation_settings": {
+      "fmax": 0.05,
+      "steps": 200,
+      "constant_volume": true
+    },
+    "filter_type": "ExpCellFilter"
+  },
+  "phonon3_settings": {
+    "dim": [2, 2, 2],
+    "distance": 0.2
+  },
+  "md_settings": {
+    "dt": 1,
+    "temp0": 35,
+    "nsteps0": 10,
+    "temp1": 200,
+    "nsteps1": 20,
+    "taut": 20,
+    "min_size": 10.0
+  }
+}
+JSON
+
+# Run the Python analysis for this JID/calculator combination
+python run_chipsff.py --input_file input_${jid}_${calculator}.json
+
+EOT
+}
+
+# Function to submit a job for mlearn analysis
+submit_mlearn_job() {
+  local element=$1
+  local calculator=$2
+
+  sbatch <<EOT
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=1-00:00:00
+#SBATCH --partition=rack1,rack2e,rack3,rack4,rack4e,rack5,rack6
+#SBATCH --job-name=mlearn_${element}_${calculator}
+#SBATCH --output=logs/mlearn_${element}_${calculator}_%j.out
+#SBATCH --error=logs/mlearn_${element}_${calculator}_%j.err
+
+# Generate input JSON file for this mlearn element/calculator combination
+cat > input_mlearn_${element}_${calculator}.json <<JSON
+{
+  "mlearn_elements": ["$element"],
+  "calculator_type": "$calculator",
+  "properties_to_calculate": ["compare_mlearn_forces"]
+}
+JSON
+
+# Run the Python analysis for this mlearn element/calculator combination
+python run_chipsff.py --input_file input_mlearn_${element}_${calculator}.json
+
+EOT
+}
+
+# Loop through each JID and calculator combination for bulk calculations
+for jid in "${jid_list[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_bulk_job "$jid" "$calculator"
+  done
+done
+
+# Loop through each mlearn element and calculator combination for mlearn analysis
+for element in "${mlearn_elements[@]}"; do
+  for calculator in "${calculator_types[@]}"; do
+    submit_mlearn_job "$element" "$calculator"
+  done
+done
+
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/run_chipsff.py
new file mode 100644
index 000000000..79b65eb07
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_12_2_24/run_chipsff.py
@@ -0,0 +1,2286 @@
+#!/usr/bin/env python
+import os
+import subprocess
+import sys
+import time
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from ase.eos import EquationOfState
+from ase import Atoms as AseAtoms
+from ase.units import kJ
+from ase.constraints import ExpCellFilter
+from ase.optimize.fire import FIRE
+from ase.stress import voigt_6_to_full_3x3_stress
+import ase.units
+from jarvis.db.figshare import get_jid_data, data, get_request_data
+from jarvis.core.atoms import Atoms, ase_to_atoms
+from jarvis.io.vasp.inputs import Poscar
+from jarvis.core.kpoints import Kpoints3D as Kpoints
+from jarvis.analysis.defects.vacancy import Vacancy
+from jarvis.analysis.defects.surface import Surface
+import pandas as pd
+import h5py
+import shutil
+import glob
+import io
+import contextlib
+import requests
+import zipfile
+import re
+import plotly.express as px
+from sklearn.metrics import mean_absolute_error, r2_score
+from matplotlib.gridspec import GridSpec
+import argparse
+from jarvis.db.jsonutils import loadjson
+from chipsff.config import CHIPSFFConfig
+
+dft_3d = data("dft_3d")
+vacancydb = data("vacancydb")
+surf_url = "https://figshare.com/ndownloader/files/46355689"
+surface_data = get_request_data(js_tag="surface_db_dd.json", url=surf_url)
+
+def get_entry(jid):
+    for entry in dft_3d:
+        if entry["jid"] == jid:
+            return entry
+    raise ValueError(f"JID {jid} not found in the database")
+
+def collect_data(dft_3d, vacancydb, surface_data):
+    defect_ids = list(set([entry["jid"] for entry in vacancydb]))
+    surf_ids = list(set([entry["name"].split("Surface-")[1].split("_miller_")[0] for entry in surface_data]))
+
+    aggregated_data = []
+    for entry in dft_3d:
+        tmp = entry
+        tmp["vacancy"] = {}
+        tmp["surface"] = {}
+
+        # Check if the entry is in the defect dataset
+        if entry["jid"] in defect_ids:
+            for vac_entry in vacancydb:
+                if entry["jid"] == vac_entry["jid"]:
+                    tmp["vacancy"].setdefault(
+                        vac_entry["id"].split("_")[0] + "_" + vac_entry["id"].split("_")[1],
+                        vac_entry["ef"]
+                    )
+
+        # Check if the entry is in the surface dataset
+        if entry["jid"] in surf_ids:
+            for surf_entry in surface_data:
+                jid = surf_entry["name"].split("Surface-")[1].split("_miller_")[0]
+                if entry["jid"] == jid:
+                    tmp["surface"].setdefault(
+                        "_".join(surf_entry["name"].split("_thickness")[0].split("_")[0:5]),
+                        surf_entry["surf_en"]
+                    )
+
+        aggregated_data.append(tmp)
+    
+    return aggregated_data
+
+def get_vacancy_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the vacancy formation energy entry (vac_en_entry) for a given jid.
+    
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+    
+    Returns:
+    dict: A dictionary containing the vacancy formation energy entry and corresponding symbol.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            vacancy_data = entry.get("vacancy", {})
+            if vacancy_data:
+                return [{"symbol": key, "vac_en_entry": value} for key, value in vacancy_data.items()]
+            else:
+                return f"No vacancy data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+    
+def get_surface_energy_entry(jid, aggregated_data):
+    """
+    Retrieve the surface energy entry (surf_en_entry) for a given jid.
+
+    Parameters:
+    jid (str): The JID of the material.
+    aggregated_data (list): The aggregated data containing vacancy and surface information.
+
+    Returns:
+    list: A list of dictionaries containing the surface energy entry and corresponding name.
+    """
+    for entry in aggregated_data:
+        if entry["jid"] == jid:
+            surface_data = entry.get("surface", {})
+            if surface_data:
+                # Prepend 'Surface-JVASP-<jid>_' to the key for correct matching
+                return [{"name": f"{key}", "surf_en_entry": value} for key, value in surface_data.items()]
+            else:
+                return f"No surface data found for JID {jid}"
+    return f"JID {jid} not found in the data."
+
+
+def log_job_info(message, log_file):
+    """Log job information to a file and print it."""
+    with open(log_file, "a") as f:
+        f.write(message + "\n")
+    print(message)
+
+def save_dict_to_json(data_dict, filename):
+    with open(filename, "w") as f:
+        json.dump(data_dict, f, indent=4)
+
+def load_dict_from_json(filename):
+    with open(filename, 'r') as f:
+        return json.load(f)
+        
+def setup_calculator(calculator_type):
+    if calculator_type == "matgl":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "matgl-direct":
+        import matgl
+        from matgl.ext.ase import M3GNetCalculator
+        pot = matgl.load_model("M3GNet-MP-2021.2.8-DIRECT-PES")
+        return M3GNetCalculator(pot, compute_stress=True, stress_weight=0.01)
+    elif calculator_type == "alignn_ff":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+        model_path = default_path() #can be adjusted to other ALIGNN models
+        return AlignnAtomwiseCalculator(
+            path=model_path,
+            stress_wt=0.1,
+            force_mult_natoms=False,
+            force_multiplier=1,
+            modl_filename="best_model.pt",
+        )
+    elif calculator_type == "alignn_ff_12_2_24":
+        from alignn.ff.ff import AlignnAtomwiseCalculator, default_path
+
+        return AlignnAtomwiseCalculator()
+    elif calculator_type == "chgnet":
+        from chgnet.model.dynamics import CHGNetCalculator
+        return CHGNetCalculator()
+    elif calculator_type == "mace":
+        from mace.calculators import mace_mp
+        return mace_mp()
+    elif calculator_type == "mace-alexandria":
+        from mace.calculators.mace import MACECalculator
+        model_path="/users/dtw2/utils/models/alexandria_v2/mace/2D_universal_force_field_cpu.model" #adjust path to mace-alexandria
+        return MACECalculator(model_path,device="cpu") 
+    elif calculator_type == "sevennet":
+        from sevenn.sevennet_calculator import SevenNetCalculator
+        checkpoint_path = "/users/dtw2/SevenNet/pretrained_potentials/SevenNet_0__11July2024/checkpoint_sevennet_0.pth" #adjust path to sevennet
+        return SevenNetCalculator(checkpoint_path, device="cpu")
+    elif calculator_type == "orb-v2":
+        from orb_models.forcefield import pretrained
+        from orb_models.forcefield.calculator import ORBCalculator
+        orbff = pretrained.orb_v2()
+        return ORBCalculator(orbff, device="cpu")
+    elif calculator_type == "eqV2_31M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat.pt") #adjust path to OMat24   
+    elif calculator_type == "eqV2_153M_omat":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_153M_omat.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_31M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_31M_omat_mp_salex.pt") #adjust path to OMat24
+    elif calculator_type == "eqV2_86M_omat_mp_salex":
+        from fairchem.core import OCPCalculator
+        return OCPCalculator(checkpoint_path="/users/dtw2/fairchem-models/pretrained_models/eqV2_86M_omat_mp_salex.pt") #adjust path to OMat24
+    else:
+        raise ValueError("Unsupported calculator type")
+
+class MaterialsAnalyzer:
+    def __init__(
+        self,
+        jid=None,
+        calculator_type=None,
+        chemical_potentials_file=None,
+        film_jid=None,
+        substrate_jid=None,
+        film_index=None,
+        substrate_index=None,
+        bulk_relaxation_settings=None,
+        phonon_settings=None,
+        properties_to_calculate=None,
+        use_conventional_cell=False,
+        surface_settings=None,
+        defect_settings=None,
+        phonon3_settings=None,
+        md_settings=None
+    ):
+        self.calculator_type = calculator_type
+        self.use_conventional_cell = use_conventional_cell
+        self.chemical_potentials_file = chemical_potentials_file
+        self.bulk_relaxation_settings = bulk_relaxation_settings or {}
+        self.phonon_settings = phonon_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.properties_to_calculate = properties_to_calculate or []
+        self.surface_settings = surface_settings or {}
+        self.defect_settings = defect_settings or {}
+        self.film_index = film_index or "1_1_0"
+        self.substrate_index = substrate_index or "1_1_0"
+        self.phonon3_settings = phonon3_settings or {'dim': [2, 2, 2], 'distance': 0.2}
+        self.md_settings = md_settings or {'dt': 1, 'temp0': 3500, 'nsteps0': 1000, 'temp1': 300, 'nsteps1': 2000, 'taut': 20, 'min_size': 10.0}
+        if jid:
+            self.jid = jid
+            # Load atoms for the given JID
+            self.atoms = self.get_atoms(jid)
+            # Get reference data for the material
+            self.reference_data = get_entry(jid)
+            # Set up the output directory and log file
+            self.output_dir = f"{jid}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(self.output_dir, f"{jid}_job_log.txt")
+            # Initialize job_info dictionary
+            self.job_info = {
+                "jid": jid,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        elif film_jid and substrate_jid:
+            # Ensure film_jid and substrate_jid are strings, not lists
+            if isinstance(film_jid, list):
+                film_jid = film_jid[0]
+            if isinstance(substrate_jid, list):
+                substrate_jid = substrate_jid[0]
+
+            self.film_jid = film_jid
+            self.substrate_jid = substrate_jid
+
+            # Include Miller indices in directory and file names
+            self.output_dir = f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_{calculator_type}"
+            os.makedirs(self.output_dir, exist_ok=True)
+            self.log_file = os.path.join(
+                self.output_dir,
+                f"Interface_{film_jid}_{self.film_index}_{substrate_jid}_{self.substrate_index}_job_log.txt",
+            )
+            self.job_info = {
+                "film_jid": film_jid,
+                "substrate_jid": substrate_jid,
+                "film_index": self.film_index,
+                "substrate_index": self.substrate_index,
+                "calculator_type": calculator_type,
+            }
+            self.calculator = self.setup_calculator()
+            self.chemical_potentials = self.load_chemical_potentials()
+        else:
+            raise ValueError("Either 'jid' or both 'film_jid' and 'substrate_jid' must be provided.")
+
+        # Set up the logger
+        self.setup_logger()
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger(self.jid or f"{self.film_jid}_{self.substrate_jid}")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        """Log information to the job log file."""
+        log_job_info(message, self.log_file)
+
+    def get_atoms(self, jid):
+        dat = get_jid_data(jid=jid, dataset="dft_3d")
+        return Atoms.from_dict(dat["atoms"])
+
+    def setup_calculator(self):
+        calc = setup_calculator(self.calculator_type)
+        self.log(f"Using calculator: {self.calculator_type}")
+        return calc
+
+    def load_chemical_potentials(self):
+        if os.path.exists(self.chemical_potentials_file):
+            with open(self.chemical_potentials_file, "r") as f:
+                return json.load(f)
+        else:
+            return {}
+
+    def save_chemical_potentials(self):
+        with open(self.chemical_potentials_file, "w") as f:
+            json.dump(self.chemical_potentials, f, indent=4)
+
+    def capture_fire_output(self, ase_atoms, fmax, steps):
+        """Capture the output of the FIRE optimizer."""
+        log_stream = io.StringIO()
+        with contextlib.redirect_stdout(log_stream):
+            dyn = FIRE(ase_atoms)
+            dyn.run(fmax=fmax, steps=steps)
+        output = log_stream.getvalue().strip()
+
+        final_energy = None
+        if output:
+            last_line = output.split("\n")[-1]
+            match = re.search(r"FIRE:\s+\d+\s+\d+:\d+:\d+\s+(-?\d+\.\d+)", last_line)
+            if match:
+                final_energy = float(match.group(1))
+
+        return final_energy, dyn.nsteps
+
+    def relax_structure(self):
+        """Perform structure relaxation and log the process."""
+        self.log(f"Starting relaxation for {self.jid}")
+
+        # Use conventional cell if specified
+
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for relaxation.")
+            self.atoms = self.atoms.get_conventional_atoms  # or appropriate method
+
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.bulk_relaxation_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.bulk_relaxation_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', False)
+        ase_atoms = self.atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+
+        # Run FIRE optimizer and capture the output using relaxation settings
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < steps
+
+        # Log the final energy and relaxation status
+        self.log(f"Final energy of FIRE optimization for structure: {final_energy}")
+        self.log(
+            f"Relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        # Update job info and save the relaxed structure
+        self.job_info["relaxed_atoms"] = relaxed_atoms.to_dict()
+        self.job_info["final_energy_structure"] = final_energy
+        self.job_info["converged"] = converged
+        self.log(f"Relaxed structure: {relaxed_atoms.to_dict()}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return relaxed_atoms if converged else None
+
+
+
+    def calculate_formation_energy(self, relaxed_atoms):
+        """
+        Calculate the formation energy per atom using the equilibrium energy and chemical potentials.
+        """
+        e0 = self.job_info["equilibrium_energy"]
+        composition = relaxed_atoms.composition.to_dict()
+        total_energy = e0
+
+        for element, amount in composition.items():
+            chemical_potential = self.get_chemical_potential(element)
+            if chemical_potential is None:
+                self.log(f"Skipping formation energy calculation due to missing chemical potential for {element}.")
+                continue  # Or handle this appropriately
+            total_energy -= chemical_potential * amount
+
+        formation_energy_per_atom = total_energy / relaxed_atoms.num_atoms
+
+    # Log and save the formation energy
+        self.job_info["formation_energy_per_atom"] = formation_energy_per_atom
+        self.log(f"Formation energy per atom: {formation_energy_per_atom}")
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return formation_energy_per_atom
+
+    def calculate_element_chemical_potential(self, element, element_jid):
+        """
+        Calculate the chemical potential of a pure element using its standard structure.
+        """
+        self.log(f"Calculating chemical potential for element: {element} using JID: {element_jid}")
+        try:
+            # Get standard structure for the element using the provided JID
+            element_atoms = self.get_atoms(element_jid)
+            ase_atoms = element_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+
+            # Perform energy calculation
+            energy = ase_atoms.get_potential_energy() / len(ase_atoms)
+            self.log(f"Calculated chemical potential for {element}: {energy} eV/atom")
+            return energy
+        except Exception as e:
+            self.log(f"Error calculating chemical potential for {element}: {e}")
+            return None
+
+    def get_chemical_potential(self, element):
+        """Fetch chemical potential from JSON based on the element and calculator."""
+        element_data = self.chemical_potentials.get(element, {})
+        chemical_potential = element_data.get(f"energy_{self.calculator_type}")
+
+        if chemical_potential is None:
+            self.log(
+                f"No chemical potential found for {element} with calculator {self.calculator_type}, calculating it now..."
+            )
+            # Get standard JID for the element from chemical_potentials.json
+            element_jid = element_data.get("jid")
+            if element_jid is None:
+                self.log(f"No standard JID found for element {element} in chemical_potentials.json")
+                return None  # Skip this element
+
+            # Calculate chemical potential
+            chemical_potential = self.calculate_element_chemical_potential(element, element_jid)
+            if chemical_potential is None:
+                self.log(f"Failed to calculate chemical potential for {element}")
+                return None
+            # Add it to the chemical potentials dictionary
+            if element not in self.chemical_potentials:
+                self.chemical_potentials[element] = {}
+            self.chemical_potentials[element][f"energy_{self.calculator_type}"] = chemical_potential
+            # Save the updated chemical potentials to file
+            self.save_chemical_potentials()
+
+        return chemical_potential
+    def calculate_forces(self, atoms):
+        """
+        Calculate the forces on the given atoms without performing relaxation.
+        """
+        self.log(f"Calculating forces for {self.jid}")
+
+    # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+    # Calculate forces
+        forces = ase_atoms.get_forces()  # This returns an array of forces
+
+    # Log and save the forces
+        self.job_info['forces'] = forces.tolist()  # Convert to list for JSON serialization
+        self.log(f"Forces calculated: {forces}")
+
+    # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return forces
+
+    def calculate_ev_curve(self, relaxed_atoms):
+        """Calculate the energy-volume (E-V) curve and log results."""
+        self.log(f"Calculating EV curve for {self.jid}")
+
+        dx = np.arange(-0.06, 0.06, 0.01)  # Strain values
+        y = []  # Energies
+        vol = []  # Volumes
+        strained_structures = []  # To store strained structures
+
+        for i in dx:
+            # Apply strain and calculate energy at each strain value
+            strained_atoms = relaxed_atoms.strain_atoms(i)
+            strained_structures.append(strained_atoms)
+            ase_atoms = strained_atoms.ase_converter()
+            ase_atoms.calc = self.calculator
+            energy = ase_atoms.get_potential_energy()
+
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+        # Convert data to numpy arrays for processing
+        y = np.array(y)
+        vol = np.array(vol)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        try:
+            eos = EquationOfState(vol, y, eos="murnaghan")
+            v0, e0, B = eos.fit()
+
+            # Bulk modulus in GPa (conversion factor from ASE units)
+            kv = B / kJ * 1.0e24  # Convert to GPa
+
+            # Log important results
+            self.log(f"Bulk modulus: {kv} GPa")
+            self.log(f"Equilibrium energy: {e0} eV")
+            self.log(f"Equilibrium volume: {v0} ų")
+
+            # Save E-V curve plot
+            fig = plt.figure()
+            eos.plot()
+            ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+            fig.savefig(ev_plot_filename)
+            plt.close(fig)
+            self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+            # Save E-V curve data to a text file
+            ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+            with open(ev_data_filename, "w") as f:
+                f.write("Volume (ų)\tEnergy (eV)\n")
+                for v, e in zip(vol, y):
+                    f.write(f"{v}\t{e}\n")
+            self.log(f"E-V curve data saved to {ev_data_filename}")
+
+            # Update job info with the results
+            self.job_info["bulk_modulus"] = kv
+            self.job_info["equilibrium_energy"] = e0
+            self.job_info["equilibrium_volume"] = v0
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        except RuntimeError as e:
+            self.log(f"Error fitting EOS for {self.jid}: {e}")
+            self.log("Skipping bulk modulus calculation due to fitting error.")
+            kv = None  # Set bulk modulus to None or handle this as you wish
+            e0, v0 = None, None  # Set equilibrium energy and volume to None
+
+        # Return additional values for thermal expansion analysis
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+
+    def calculate_elastic_tensor(self, relaxed_atoms):
+        import elastic
+        from elastic import get_elementary_deformations, get_elastic_tensor
+        """
+        Calculate the elastic tensor for the relaxed structure using the provided calculator.
+        """
+        self.log(f"Starting elastic tensor calculation for {self.jid}")
+        start_time = time.time()
+
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Get elementary deformations for elastic tensor calculation
+        systems = elastic.get_elementary_deformations(ase_atoms)
+
+        # Calculate the elastic tensor and convert to GPa
+        cij_order = elastic.elastic.get_cij_order(ase_atoms)
+        Cij, Bij = elastic.get_elastic_tensor(ase_atoms, systems)
+        elastic_tensor = {
+            i: j / ase.units.GPa for i, j in zip(cij_order, Cij)
+        }  # Convert to GPa
+
+        # Save and log the results
+        self.job_info["elastic_tensor"] = elastic_tensor
+        self.log(
+            f"Elastic tensor for {self.jid} with {self.calculator_type}: {elastic_tensor}"
+        )
+
+        # Timing the calculation
+        end_time = time.time()
+        self.log(f"Elastic Calculation time: {end_time - start_time} seconds")
+
+        # Save to job info JSON
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return elastic_tensor
+
+    def run_phonon_analysis(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Perform Phonon calculation, generate force constants, and plot band structure & DOS."""
+        self.log(f"Starting phonon analysis for {self.jid}")
+        phonopy_bands_figname = f"ph_{self.jid}_{self.calculator_type}.png"
+
+        # Phonon generation parameters
+        dim = self.phonon_settings.get('dim', [2, 2, 2])
+        # Define the conversion factor from THz to cm^-1
+        THz_to_cm = 33.35641  # 1 THz = 33.35641 cm^-1
+
+        force_constants_filename = "FORCE_CONSTANTS"
+        eigenvalues_filename = "phonon_eigenvalues.txt"
+        thermal_props_filename = "thermal_properties.txt"
+        write_fc = True
+        min_freq_tol_cm = -5.0  # in cm^-1
+        distance = self.phonon_settings.get('distance', 0.2)
+
+        # Generate k-point path
+        kpoints = Kpoints().kpath(relaxed_atoms, line_density=5)
+
+        # Convert atoms to Phonopy-compatible object
+        self.log("Converting atoms to Phonopy-compatible format...")
+        bulk = relaxed_atoms.phonopy_converter()
+        phonon = Phonopy(
+            bulk,
+            [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]],
+            # Do not set factor here to keep frequencies in THz during calculations
+        )
+
+        # Generate displacements
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+        self.log(f"Generated {len(supercells)} supercells for displacements.")
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for idx, scell in enumerate(supercells):
+            self.log(f"Calculating forces for supercell {idx+1}...")
+            ase_atoms = AseAtoms(
+                symbols=scell.symbols,
+                positions=scell.positions,
+                cell=scell.cell,
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+
+            # Correct for drift force
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+
+            set_of_forces.append(forces)
+
+        # Generate force constants
+        self.log("Producing force constants...")
+        phonon.produce_force_constants(forces=set_of_forces)
+
+        # Write force constants to file if required
+        if write_fc:
+            force_constants_filepath = os.path.join(
+                self.output_dir, force_constants_filename
+            )
+            self.log(f"Writing force constants to {force_constants_filepath}...")
+            write_FORCE_CONSTANTS(
+                phonon.force_constants, filename=force_constants_filepath
+            )
+            self.log(f"Force constants saved to {force_constants_filepath}")
+
+        # Prepare bands for band structure calculation
+        bands = [kpoints.kpts]  # Assuming kpoints.kpts is a list of q-points
+
+        # Prepare labels and path_connections
+        labels = []
+        path_connections = []
+        for i, label in enumerate(kpoints.labels):
+            if label:
+                labels.append(label)
+            else:
+                labels.append("")  # Empty string for points without labels
+
+        # Since we have a single path, set path_connections accordingly
+        path_connections = [True] * (len(bands) - 1)
+        path_connections.append(False)
+
+        # Run band structure calculation with labels
+        self.log("Running band structure calculation...")
+        phonon.run_band_structure(
+            bands,
+            with_eigenvectors=False,
+            labels=labels,
+            path_connections=path_connections,
+        )
+
+        # Write band.yaml file (frequencies will be in THz)
+        band_yaml_filepath = os.path.join(self.output_dir, "band.yaml")
+        self.log(f"Writing band structure data to {band_yaml_filepath}...")
+        phonon.band_structure.write_yaml(filename=band_yaml_filepath)
+        self.log(f"band.yaml saved to {band_yaml_filepath}")
+
+        # --- Begin post-processing to convert frequencies to cm^-1 while preserving formatting ---
+        from ruamel.yaml import YAML
+
+        self.log(f"Converting frequencies in {band_yaml_filepath} to cm^-1 while preserving formatting...")
+        yaml = YAML()
+        yaml.preserve_quotes = True
+
+        with open(band_yaml_filepath, 'r') as f:
+            band_data = yaml.load(f)
+
+        for phonon_point in band_data['phonon']:
+            for band in phonon_point['band']:
+                freq = band['frequency']
+                if freq is not None:
+                    band['frequency'] = freq * THz_to_cm
+
+        with open(band_yaml_filepath, 'w') as f:
+            yaml.dump(band_data, f)
+
+        self.log(f"Frequencies in {band_yaml_filepath} converted to cm^-1 with formatting preserved")
+        # --- End post-processing ---
+
+        # Phonon band structure and eigenvalues
+        lbls = kpoints.labels
+        lbls_ticks = []
+        freqs = []
+        lbls_x = []
+        count = 0
+        eigenvalues = []
+
+        for ii, k in enumerate(kpoints.kpts):
+            k_str = ",".join(map(str, k))
+            if ii == 0 or k_str != ",".join(map(str, kpoints.kpts[ii - 1])):
+                freqs_at_k = phonon.get_frequencies(k)  # Frequencies in THz
+                freqs_at_k_cm = freqs_at_k * THz_to_cm  # Convert to cm^-1
+                freqs.append(freqs_at_k_cm)
+                eigenvalues.append((k, freqs_at_k_cm))  # Store frequencies in cm^-1
+                lbl = "$" + str(lbls[ii]) + "$" if lbls[ii] else ""
+                if lbl:
+                    lbls_ticks.append(lbl)
+                    lbls_x.append(count)
+                count += 1
+
+        # Write eigenvalues to file with frequencies in cm^-1
+        eigenvalues_filepath = os.path.join(self.output_dir, eigenvalues_filename)
+        self.log(f"Writing phonon eigenvalues to {eigenvalues_filepath}...")
+        with open(eigenvalues_filepath, "w") as eig_file:
+            eig_file.write("k-points\tFrequencies (cm^-1)\n")
+            for k, freqs_at_k_cm in eigenvalues:
+                k_str = ",".join(map(str, k))
+                freqs_str = "\t".join(map(str, freqs_at_k_cm))
+                eig_file.write(f"{k_str}\t{freqs_str}\n")
+        self.log(f"Phonon eigenvalues saved to {eigenvalues_filepath}")
+
+        # Convert frequencies to numpy array in cm^-1
+        freqs = np.array(freqs)
+
+        # Plot phonon band structure and DOS
+        the_grid = plt.GridSpec(1, 2, width_ratios=[3, 1], wspace=0.0)
+        plt.rcParams.update({"font.size": 18})
+        plt.figure(figsize=(10, 5))
+
+        # Plot phonon bands
+        plt.subplot(the_grid[0])
+        for i in range(freqs.shape[1]):
+            plt.plot(freqs[:, i], lw=2, c="b")
+        for i in lbls_x:
+            plt.axvline(x=i, c="black")
+        plt.xticks(lbls_x, lbls_ticks)
+        plt.ylabel("Frequency (cm$^{-1}$)")
+        plt.xlim([0, max(lbls_x)])
+
+        # Run mesh and DOS calculations
+        phonon.run_mesh([40, 40, 40], is_gamma_center=True, is_mesh_symmetry=False)
+        phonon.run_total_dos()
+        tdos = phonon.total_dos
+        freqs_dos = np.array(tdos.frequency_points) * THz_to_cm  # Convert to cm^-1
+        dos_values = tdos.dos
+        min_freq = min_freq_tol_cm  # in cm^-1
+        max_freq = max(freqs_dos)
+
+        plt.ylim([min_freq, max_freq])
+
+        # Plot DOS
+        plt.subplot(the_grid[1])
+        plt.fill_between(
+            dos_values, freqs_dos, color=(0.2, 0.4, 0.6, 0.6), edgecolor="k", lw=1, y2=0
+        )
+        plt.xlabel("DOS")
+        plt.yticks([])
+        plt.xticks([])
+        plt.ylim([min_freq, max_freq])
+        plt.xlim([0, max(dos_values)])
+
+        # Save the plot
+        os.makedirs(self.output_dir, exist_ok=True)
+        plot_filepath = os.path.join(self.output_dir, phonopy_bands_figname)
+        plt.tight_layout()
+        plt.savefig(plot_filepath)
+        self.log(
+            f"Phonon band structure and DOS combined plot saved to {plot_filepath}"
+        )
+        plt.close()
+
+        self.log("Calculating thermal properties...")
+        phonon.run_mesh(mesh=[20, 20, 20])
+        phonon.run_thermal_properties(t_step=10, t_max=1000, t_min=0)
+        tprop_dict = phonon.get_thermal_properties_dict()
+
+        # Plot thermal properties
+        plt.figure()
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["free_energy"],
+            label="Free energy (kJ/mol)",
+            color="red",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["entropy"],
+            label="Entropy (J/K*mol)",
+            color="blue",
+        )
+        plt.plot(
+            tprop_dict["temperatures"],
+            tprop_dict["heat_capacity"],
+            label="Heat capacity (J/K*mol)",
+            color="green",
+        )
+        plt.legend()
+        plt.xlabel("Temperature (K)")
+        plt.ylabel("Thermal Properties")
+        plt.title("Thermal Properties")
+
+        thermal_props_plot_filepath = os.path.join(
+            self.output_dir, f"Thermal_Properties_{self.jid}.png"
+        )
+        plt.savefig(thermal_props_plot_filepath)
+        self.log(f"Thermal properties plot saved to {thermal_props_plot_filepath}")
+        plt.close()
+
+        # Save thermal properties to file
+        thermal_props_filepath = os.path.join(self.output_dir, thermal_props_filename)
+        with open(thermal_props_filepath, "w") as f:
+            f.write(
+                "Temperature (K)\tFree Energy (kJ/mol)\tEntropy (J/K*mol)\tHeat Capacity (J/K*mol)\n"
+            )
+            for i in range(len(tprop_dict["temperatures"])):
+                f.write(
+                    f"{tprop_dict['temperatures'][i]}\t{tprop_dict['free_energy'][i]}\t"
+                    f"{tprop_dict['entropy'][i]}\t{tprop_dict['heat_capacity'][i]}\n"
+                )
+        self.log(f"Thermal properties written to {thermal_props_filepath}")
+
+        # Calculate zero-point energy (ZPE)
+        zpe = tprop_dict["free_energy"][0] * 0.0103643  # Converting from kJ/mol to eV
+        self.log(f"Zero-point energy: {zpe} eV")
+
+        # Save to job info
+        self.job_info["phonopy_bands"] = phonopy_bands_figname
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return phonon, zpe
+
+    def analyze_defects(self):
+        """Analyze defects by generating, relaxing, and calculating vacancy formation energy."""
+        self.log("Starting defect analysis...")
+        generate_settings = self.defect_settings.get('generate_settings', {})
+        on_conventional_cell = generate_settings.get('on_conventional_cell', True)
+        enforce_c_size = generate_settings.get('enforce_c_size', 8)
+        extend = generate_settings.get('extend', 1)
+    # Generate defect structures from the original atoms
+        defect_structures = Vacancy(self.atoms).generate_defects(on_conventional_cell=on_conventional_cell, enforce_c_size=enforce_c_size, extend=extend)
+
+        for defect in defect_structures:
+        # Extract the defect structure and related metadata
+            defect_structure = Atoms.from_dict(defect.to_dict()["defect_structure"])
+        
+        # Construct a consistent defect name without Wyckoff notation
+            element = defect.to_dict()['symbol']
+            defect_name = f"{self.jid}_{element}"  # Consistent format
+            self.log(f"Analyzing defect: {defect_name}")
+
+        # Relax the defect structure
+            relaxed_defect_atoms = self.relax_defect_structure(defect_structure, name=defect_name)
+
+            if relaxed_defect_atoms is None:
+                self.log(f"Skipping {defect_name} due to failed relaxation.")
+                continue
+
+        # Retrieve energies for calculating the vacancy formation energy
+            vacancy_energy = self.job_info.get(f"final_energy_defect for {defect_name}")
+            bulk_energy = (
+                self.job_info.get("equilibrium_energy")
+                / self.atoms.num_atoms
+                * (defect_structure.num_atoms + 1)
+            )
+
+            if vacancy_energy is None or bulk_energy is None:
+                self.log(f"Skipping {defect_name} due to missing energy values.")
+                continue
+
+        # Get chemical potential and calculate vacancy formation energy
+            chemical_potential = self.get_chemical_potential(element)
+
+            if chemical_potential is None:
+                self.log(f"Skipping {defect_name} due to missing chemical potential for {element}.")
+                continue
+
+            vacancy_formation_energy = vacancy_energy - bulk_energy + chemical_potential
+
+        # Log and store the vacancy formation energy consistently
+            self.job_info[f"vacancy_formation_energy for {defect_name}"] = vacancy_formation_energy
+            self.log(f"Vacancy formation energy for {defect_name}: {vacancy_formation_energy} eV")
+
+    # Save the job info to a JSON file
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+        self.log("Defect analysis completed.")
+
+    def relax_defect_structure(self, atoms, name):
+        """Relax the defect structure and log the process."""
+        # Convert atoms to ASE format and assign the calculator
+        filter_type = self.defect_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.defect_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+            # Implement other filters if needed
+            pass
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log the final energy and relaxation status
+        self.log(
+            f"Final energy of FIRE optimization for defect structure: {final_energy}"
+        )
+        self.log(
+            f"Defect relaxation {'converged' if converged else 'did not converge'} within 200 steps."
+        )
+
+        # Update job info with the final energy and convergence status
+        self.job_info[f"final_energy_defect for {name}"] = final_energy
+        self.job_info[f"converged for {name}"] = converged
+
+        if converged:
+            poscar_filename = os.path.join(self.output_dir, f"POSCAR_{name}_relaxed.vasp")
+            poscar_defect = Poscar(relaxed_atoms)
+            poscar_defect.write_file(poscar_filename)
+            self.log(f"Relaxed defect structure saved to {poscar_filename}")
+
+        return relaxed_atoms if converged else None
+
+    def analyze_surfaces(self):
+        """
+        Perform surface analysis by generating surface structures, relaxing them, and calculating surface energies.
+        """
+        self.log(f"Analyzing surfaces for {self.jid}")
+
+        indices_list = self.surface_settings.get('indices_list', [
+            [1, 0, 0],
+            [1, 1, 1],
+            [1, 1, 0],
+            [0, 1, 1],
+            [0, 0, 1],
+            [0, 1, 0],
+        ])
+        layers = self.surface_settings.get('layers', 4)
+        vacuum = self.surface_settings.get('vacuum', 18)
+
+        for indices in indices_list:
+            # Generate surface and check for polarity
+            surface = (
+                Surface(atoms=self.atoms, indices=indices, layers=layers, vacuum=vacuum)
+                .make_surface()
+                .center_around_origin()
+            )
+            if surface.check_polar:
+                self.log(
+                    f"Skipping polar surface for {self.jid} with indices {indices}"
+                )
+                continue
+
+            # Write initial POSCAR for surface
+            poscar_surface = Poscar(atoms=surface)
+            poscar_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}.vasp",
+                )
+            )
+
+            # Relax the surface structure
+            relaxed_surface_atoms, final_energy = self.relax_surface_structure(
+                surface, indices
+            )
+
+            # If relaxation failed, skip further calculations
+            if relaxed_surface_atoms is None:
+                self.log(f"Skipping surface {indices} due to failed relaxation.")
+                continue
+
+            # Write relaxed POSCAR for surface
+            pos_relaxed_surface = Poscar(relaxed_surface_atoms)
+            pos_relaxed_surface.write_file(
+                os.path.join(
+                    self.output_dir,
+                    f"POSCAR_{self.jid}_surface_{indices}_{self.calculator_type}_relaxed.vasp",
+                )
+            )
+
+            # Calculate and log surface energy
+            bulk_energy = self.job_info.get("equilibrium_energy")
+            if final_energy is None or bulk_energy is None:
+                self.log(
+                    f"Skipping surface energy calculation for {self.jid} with indices {indices} due to missing energy values."
+                )
+                continue
+
+            surface_energy = self.calculate_surface_energy(
+                final_energy, bulk_energy, relaxed_surface_atoms, surface
+            )
+
+            # Store the surface energy with the new naming convention
+            surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+            self.job_info[surface_name] = surface_energy
+            self.log(
+                f"Surface energy for {self.jid} with indices {indices}: {surface_energy} J/m^2"
+            )
+
+        # Save updated job info
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log("Surface analysis completed.")
+
+    def relax_surface_structure(self, atoms, indices):
+        """
+        Relax the surface structure and log the process.
+        """
+        filter_type = self.surface_settings.get('filter_type', 'ExpCellFilter')
+        relaxation_settings = self.surface_settings.get('relaxation_settings', {})
+        constant_volume = relaxation_settings.get('constant_volume', True)
+        self.log(f"Starting surface relaxation for {self.jid} with indices {indices}")
+        start_time = time.time()
+        fmax = relaxation_settings.get('fmax', 0.05)
+        steps = relaxation_settings.get('steps', 200)
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        if filter_type == 'ExpCellFilter':
+            ase_atoms = ExpCellFilter(ase_atoms, constant_volume=constant_volume)
+        else:
+        # Implement other filters if needed
+            pass
+        # Run FIRE optimizer and capture the output
+        final_energy, nsteps = self.capture_fire_output(ase_atoms, fmax=fmax, steps=steps)
+        relaxed_atoms = ase_to_atoms(ase_atoms.atoms)
+        converged = nsteps < 200
+
+        # Log relaxation results
+        self.log(
+            f"Final energy of FIRE optimization for surface structure: {final_energy}"
+        )
+        self.log(
+            f"Surface relaxation {'converged' if converged else 'did not converge'} within {nsteps} steps."
+        )
+
+        end_time = time.time()
+        self.log(
+            f"Surface Relaxation Calculation time: {end_time - start_time} seconds"
+        )
+
+        # Update job info and return relaxed atoms if converged, otherwise return None
+        self.job_info[f"final_energy_surface_{indices}"] = final_energy
+        self.job_info[f"converged_surface_{indices}"] = converged
+
+        # Return both relaxed atoms and the final energy as a tuple
+        return (relaxed_atoms if converged else None), final_energy
+
+    def calculate_surface_energy(
+        self, final_energy, bulk_energy, relaxed_atoms, surface
+    ):
+        """
+        Calculate the surface energy based on the final energy of the relaxed surface and bulk energy.
+        """
+        # Calculate the number of bulk units in the surface supercell
+        num_units = surface.num_atoms / self.atoms.num_atoms
+
+        # Calculate the surface area using the lattice vectors
+        m = relaxed_atoms.lattice.matrix
+        surface_area = np.linalg.norm(np.cross(m[0], m[1]))
+
+        # Calculate surface energy in J/m^2
+        surface_energy = (
+            (final_energy - bulk_energy * num_units) * 16.02176565 / (2 * surface_area)
+        )
+
+        return surface_energy
+
+    def run_phonon3_analysis(self, relaxed_atoms):
+        from phono3py import Phono3py
+        """Run Phono3py analysis, process results, and generate thermal conductivity data."""
+        self.log(f"Starting Phono3py analysis for {self.jid}")
+
+        # Set parameters for the Phono3py calculation
+        dim = self.phonon3_settings.get('dim', [2, 2, 2])
+        distance = self.phonon3_settings.get('distance', 0.2)
+
+        #force_multiplier = 16
+
+        # Convert atoms to Phonopy-compatible object and set up Phono3py
+        ase_atoms = relaxed_atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+        bulk = relaxed_atoms.phonopy_converter()
+
+        phonon = Phono3py(bulk, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]])
+        phonon.generate_displacements(distance=distance)
+        supercells = phonon.supercells_with_displacements
+
+        # Calculate forces for each supercell
+        set_of_forces = []
+        for scell in supercells:
+            ase_atoms = AseAtoms(
+                symbols=scell.get_chemical_symbols(),
+                scaled_positions=scell.get_scaled_positions(),
+                cell=scell.get_cell(),
+                pbc=True,
+            )
+            ase_atoms.calc = self.calculator
+            forces = np.array(ase_atoms.get_forces())
+            drift_force = forces.sum(axis=0)
+            for force in forces:
+                force -= drift_force / forces.shape[0]
+            set_of_forces.append(forces)
+
+        # Set the forces and produce third-order force constants
+        forces = np.array(set_of_forces).reshape(-1, len(phonon.supercell), 3)
+        phonon.forces = forces
+        phonon.produce_fc3()
+
+        # Run thermal conductivity calculation
+        phonon.mesh_numbers = 30
+        phonon.init_phph_interaction()
+        phonon.run_thermal_conductivity(
+            temperatures=range(0, 1001, 10), write_kappa=True
+        )
+
+        # Collect thermal conductivity data
+        kappa = phonon.thermal_conductivity.kappa
+        self.log(f"Thermal conductivity: {kappa}")
+
+        # Move generated HDF5 files to the output directory
+        hdf5_file_pattern = "kappa-*.hdf5"
+        for hdf5_file in glob.glob(hdf5_file_pattern):
+            shutil.move(hdf5_file, os.path.join(self.output_dir, hdf5_file))
+
+        # Process Phono3py results and save plots
+        self.process_phonon3_results()
+
+        # Save updated job info to JSON
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(f"Phono3py analysis completed for {self.jid}")
+
+    def process_phonon3_results(self):
+        """Process Phono3py results and generate plots of thermal conductivity."""
+        file_pattern = os.path.join(self.output_dir, "kappa-*.hdf5")
+        file_list = glob.glob(file_pattern)
+
+        temperatures = np.arange(10, 101, 10)
+        kappa_xx_values = []
+
+        if file_list:
+            hdf5_filename = file_list[0]
+            self.log(f"Processing file: {hdf5_filename}")
+
+            for temperature_index in temperatures:
+                converted_kappa = self.convert_kappa_units(
+                    hdf5_filename, temperature_index
+                )
+                kappa_xx = converted_kappa[0]
+                kappa_xx_values.append(kappa_xx)
+                self.log(
+                    f"Temperature index {temperature_index}, converted kappa: {kappa_xx}"
+                )
+
+            # Save results to job_info
+            self.job_info["temperatures"] = temperatures.tolist()
+            self.job_info["kappa_xx_values"] = kappa_xx_values
+
+            # Plot temperature vs. converted kappa (xx element)
+            plt.figure(figsize=(8, 6))
+            plt.plot(
+                temperatures * 10, kappa_xx_values, marker="o", linestyle="-", color="b"
+            )
+            plt.xlabel("Temperature (K)")
+            plt.ylabel("Converted Kappa (xx element)")
+            plt.title("Temperature vs. Converted Kappa (xx element)")
+            plt.grid(True)
+            plt.savefig(
+                os.path.join(self.output_dir, "Temperature_vs_Converted_Kappa.png")
+            )
+            plt.close()
+        else:
+            self.log("No files matching the pattern were found.")
+
+    def convert_kappa_units(self, hdf5_filename, temperature_index):
+        """Convert thermal conductivity kappa from HDF5 file units."""
+        with h5py.File(hdf5_filename, "r") as f:
+            kappa_unit_conversion = f["kappa_unit_conversion"][()]
+            heat_capacity = f["heat_capacity"][:]
+            gv_by_gv = f["gv_by_gv"][:]
+            gamma = f["gamma"][:]
+
+            converted_kappa = (
+                kappa_unit_conversion
+                * heat_capacity[temperature_index, 2, 0]
+                * gv_by_gv[2, 0]
+                / (2 * gamma[temperature_index, 2, 0])
+            )
+
+            return converted_kappa
+
+    def calculate_thermal_expansion(self, relaxed_atoms):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        """Calculate the thermal expansion coefficient using QHA."""
+
+        def log(message):
+            with open(self.log_file, "a") as f:
+                f.write(message + "\n")
+            print(message)
+
+        log("Starting thermal expansion analysis...")
+
+        # Step 1: Calculate finer E-V curve
+        vol, y, strained_structures, eos, kv, e0, v0 = self.fine_ev_curve(
+            atoms=relaxed_atoms, dx=np.linspace(-0.05, 0.05, 50)  # Denser grid
+        )
+
+        # Log Bulk modulus, equilibrium energy, and volume
+        log(
+            f"Bulk modulus: {kv} GPa, Equilibrium energy: {y[0]} eV, Volume: {vol[0]} ų"
+        )
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = y[0]
+        self.job_info["equilibrium_volume"] = vol[0]
+
+        # Step 2: Generate phonons for strained structures
+        free_energies, heat_capacities, entropies, temperatures = (
+            self.generate_phonons_for_volumes(
+                strained_structures,
+                calculator=self.calculator,
+                dim=[2, 2, 2],
+                distance=0.2,
+                mesh=[20, 20, 20],
+            )
+        )
+
+        # Step 3: Perform QHA-based thermal expansion analysis
+        alpha = self.perform_qha(
+            volumes=vol,
+            energies=y,
+            free_energies=free_energies,
+            heat_capacities=heat_capacities,
+            entropies=entropies,
+            temperatures=temperatures,
+            output_dir=self.output_dir,
+        )
+
+        self.log(f"Thermal expansion coefficient calculated: {alpha}")
+        save_dict_to_json(
+            self.job_info,
+            os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            ),
+        )
+        self.log(
+            f"Thermal expansion analysis information saved to file: {self.jid}_{self.calculator_type}_job_info.json"
+        )
+
+    # Helper Functions Inside the Class
+    def fine_ev_curve(self, atoms, dx=np.linspace(-0.05, 0.05, 50)):
+        """
+        Generate a finer energy-volume curve for strained structures.
+        """
+        y = []
+        vol = []
+        strained_structures = []
+
+        for i in dx:
+            # Apply strain and get strained atoms
+            strained_atoms = atoms.strain_atoms(i)
+            ase_atoms = strained_atoms.ase_converter()  # Convert to ASE Atoms
+            ase_atoms.calc = self.calculator  # Assign the calculator
+
+            # Get potential energy and volume
+            energy = ase_atoms.get_potential_energy()
+            y.append(energy)
+            vol.append(strained_atoms.volume)
+
+            strained_structures.append(strained_atoms)  # Save the strained structure
+
+        vol = np.array(vol)
+        y = np.array(y)
+
+        # Fit the E-V curve using an equation of state (EOS)
+        eos = EquationOfState(vol, y, eos="murnaghan")
+        v0, e0, B = eos.fit()
+        kv = B / kJ * 1.0e24  # Convert to GPa
+
+        # Log important results
+        self.log(f"Bulk modulus: {kv} GPa")
+        self.log(f"Equilibrium energy: {e0} eV")
+        self.log(f"Equilibrium volume: {v0} ų")
+
+        # Save E-V curve plot
+        fig = plt.figure()
+        eos.plot()
+        ev_plot_filename = os.path.join(self.output_dir, "E_vs_V_curve.png")
+        fig.savefig(ev_plot_filename)
+        plt.close(fig)
+        self.log(f"E-V curve plot saved to {ev_plot_filename}")
+
+        # Save E-V curve data to a text file
+        ev_data_filename = os.path.join(self.output_dir, "E_vs_V_data.txt")
+        with open(ev_data_filename, "w") as f:
+            f.write("Volume (ų)\tEnergy (eV)\n")
+            for v, e in zip(vol, y):
+                f.write(f"{v}\t{e}\n")
+        self.log(f"E-V curve data saved to {ev_data_filename}")
+
+        # Update job info with the results
+        self.job_info["bulk_modulus"] = kv
+        self.job_info["equilibrium_energy"] = e0
+        self.job_info["equilibrium_volume"] = v0
+        save_dict_to_json(self.job_info, self.get_job_info_filename())
+
+        return vol, y, strained_structures, eos, kv, e0, v0
+
+    def generate_phonons_for_volumes(
+        self, structures, calculator, dim=[2, 2, 2], distance=0.2, mesh=[20, 20, 20]
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        all_free_energies = []
+        all_heat_capacities = []
+        all_entropies = []
+        temperatures = np.arange(0, 300, 6)  # Define temperature range
+
+        for structure in structures:
+            # Convert structure to PhonopyAtoms
+            phonopy_atoms = PhonopyAtoms(
+                symbols=[str(e) for e in structure.elements],
+                positions=structure.cart_coords,
+                cell=structure.lattice.matrix,
+            )
+
+            # Initialize Phonopy object
+            phonon = Phonopy(
+                phonopy_atoms, [[dim[0], 0, 0], [0, dim[1], 0], [0, 0, dim[2]]]
+            )
+            phonon.generate_displacements(distance=distance)
+
+            # Calculate forces for displaced structures
+            supercells = phonon.get_supercells_with_displacements()
+            forces = []
+            for scell in supercells:
+                ase_atoms = AseAtoms(
+                    symbols=scell.symbols,
+                    positions=scell.positions,
+                    cell=scell.cell,
+                    pbc=True,
+                )
+                ase_atoms.calc = calculator
+                forces.append(ase_atoms.get_forces())
+
+            phonon.produce_force_constants(forces=forces)
+
+            # Post-processing to get thermal properties
+            phonon.run_mesh(mesh=mesh)
+            phonon.run_thermal_properties(t_min=0, t_step=6, t_max=294)
+            tprop_dict = phonon.get_thermal_properties_dict()
+
+            free_energies = tprop_dict["free_energy"]
+            heat_capacities = tprop_dict["heat_capacity"]
+            entropies = tprop_dict["entropy"]
+
+            all_entropies.append(entropies)
+            all_free_energies.append(free_energies)
+            all_heat_capacities.append(heat_capacities)
+
+        return (
+            np.array(all_free_energies),
+            np.array(all_heat_capacities),
+            np.array(all_entropies),
+            temperatures,
+        )
+
+    def perform_qha(
+        self,
+        volumes,
+        energies,
+        free_energies,
+        heat_capacities,
+        entropies,
+        temperatures,
+        output_dir,
+    ):
+        from phonopy import Phonopy, PhonopyQHA
+        from phonopy.file_IO import write_FORCE_CONSTANTS
+        from phonopy.phonon.band_structure import BandStructure
+        from phonopy.structure.atoms import Atoms as PhonopyAtoms
+        # Debugging: print array sizes
+        print(f"Number of temperatures: {len(temperatures)}")
+        print(f"Number of free energy data points: {free_energies.shape}")
+        print(f"Number of volume data points: {len(volumes)}")
+
+        # Ensure that volumes, free_energies, and temperatures are consistent
+        if len(volumes) != len(temperatures):
+            raise ValueError(
+                "The number of volumes must match the number of temperatures"
+            )
+
+        # Initialize the QHA object
+        try:
+            qha = PhonopyQHA(
+                volumes=volumes,
+                electronic_energies=energies,
+                free_energy=free_energies,
+                cv=heat_capacities,
+                entropy=entropies,
+                temperatures=temperatures,
+                eos="murnaghan",  # or another EOS if needed
+                verbose=True,
+            )
+        except IndexError as e:
+            print(f"Error in QHA initialization: {e}")
+            raise
+
+        # Calculate thermal expansion and save plots
+        thermal_expansion_plot = os.path.join(output_dir, "thermal_expansion.png")
+        volume_temperature_plot = os.path.join(output_dir, "volume_temperature.png")
+        helmholtz_volume_plot = os.path.join(output_dir, "helmholtz_volume.png")
+
+        qha.get_thermal_expansion()
+
+        # Save thermal expansion plot
+        qha.plot_thermal_expansion()
+        plt.savefig(thermal_expansion_plot)
+
+        # Save volume-temperature plot
+        qha.plot_volume_temperature()
+        plt.savefig(volume_temperature_plot)
+
+        # Save Helmholtz free energy vs. volume plot
+        qha.plot_helmholtz_volume()
+        plt.savefig(helmholtz_volume_plot)
+
+        # Optionally save thermal expansion coefficient to a file
+        thermal_expansion_file = os.path.join(output_dir, "thermal_expansion.txt")
+        alpha = qha.write_thermal_expansion(filename=thermal_expansion_file)
+
+        return alpha
+
+    def general_melter(self, relaxed_atoms):
+        """Perform MD simulation to melt the structure, then quench it back to room temperature."""
+        self.log(f"Starting MD melting and quenching simulation for {self.jid}")
+
+        calculator = self.setup_calculator()
+        ase_atoms = relaxed_atoms.ase_converter()
+        dim = self.ensure_cell_size(ase_atoms, min_size=self.md_settings.get('min_size', 10.0))
+        supercell = relaxed_atoms.make_supercell_matrix(dim)
+        ase_atoms = supercell.ase_converter()
+        ase_atoms.calc = calculator
+
+        dt = self.md_settings.get('dt', 1) * ase.units.fs
+        temp0 = self.md_settings.get('temp0', 3500)
+        nsteps0 = self.md_settings.get('nsteps0', 1000)
+        temp1 = self.md_settings.get('temp1', 300)
+        nsteps1 = self.md_settings.get('nsteps1', 2000)
+        taut = self.md_settings.get('taut', 20) * ase.units.fs
+        trj = os.path.join(self.output_dir, f"{self.jid}_melt.traj")
+
+        # Initialize velocities and run the first part of the MD simulation
+        from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+        from ase.md.nvtberendsen import NVTBerendsen
+
+        MaxwellBoltzmannDistribution(ase_atoms, temp0 * ase.units.kB)
+        dyn = NVTBerendsen(ase_atoms, dt, temp0, taut=taut, trajectory=trj)
+
+        def myprint():
+            message = f"time={dyn.get_time() / ase.units.fs: 5.0f} fs T={ase_atoms.get_temperature(): 3.0f} K"
+            self.log(message)
+
+        dyn.attach(myprint, interval=20)
+        dyn.run(nsteps0)
+
+        # Cool down to room temperature
+        dyn.set_temperature(temp1)
+        dyn.run(nsteps1)
+
+        # Convert back to JARVIS atoms and save the final structure
+        final_atoms = ase_to_atoms(ase_atoms)
+        poscar_filename = os.path.join(
+            self.output_dir, f"POSCAR_{self.jid}_quenched_{self.calculator_type}.vasp"
+        )
+        from ase.io import write
+
+        write(poscar_filename, final_atoms.ase_converter(), format="vasp")
+        self.log(f"MD simulation completed. Final structure saved to {poscar_filename}")
+        self.job_info["quenched_atoms"] = final_atoms.to_dict()
+
+        return final_atoms
+
+    def calculate_rdf(self, quenched_atoms):
+        """Calculate Radial Distribution Function (RDF) for quenched structure and save plot."""
+        self.log(f"Starting RDF calculation for {self.jid}")
+        ase_atoms = quenched_atoms.ase_converter()
+        rmax = 3.5
+        nbins = 200
+
+        def perform_rdf_calculation(rmax):
+            from ase.ga.utilities import get_rdf
+
+            rdfs, distances = get_rdf(ase_atoms, rmax, nbins)
+            plt.figure()
+            plt.plot(distances, rdfs)
+            plt.xlabel("Distance (Å)")
+            plt.ylabel("RDF")
+            plt.title(
+                f"Radial Distribution Function for {self.jid} ({self.calculator_type})"
+            )
+            rdf_plot_filename = os.path.join(
+                self.output_dir, f"RDF_{self.jid}_{self.calculator_type}.png"
+            )
+            plt.savefig(rdf_plot_filename)
+            plt.close()
+            self.job_info["rdf_plot"] = rdf_plot_filename
+            self.log(f"RDF plot saved to {rdf_plot_filename}")
+            return rdf_plot_filename
+
+        try:
+            perform_rdf_calculation(rmax)
+        except ValueError as e:
+            if "The cell is not large enough" in str(e):
+                recommended_rmax = float(str(e).split("<")[1].split("=")[1])
+                self.log(f"Error: {e}. Adjusting rmax to {recommended_rmax}.")
+                perform_rdf_calculation(recommended_rmax)
+            else:
+                self.log(f"Error: {e}")
+                raise
+
+    def ensure_cell_size(self, ase_atoms, min_size):
+        """Ensure that all cell dimensions are at least min_size."""
+        cell_lengths = ase_atoms.get_cell().lengths()
+        scale_factors = np.ceil(min_size / cell_lengths).astype(int)
+        supercell_dims = [max(1, scale) for scale in scale_factors]
+        return supercell_dims
+
+    def analyze_interfaces(self):
+        """Perform interface analysis using intermat package."""
+        if not self.film_jid or not self.substrate_jid:
+            self.log("Film JID or substrate JID not provided, skipping interface analysis.")
+            return
+
+        self.log(f"Starting interface analysis between {self.film_jid} and {self.substrate_jid}")
+
+        # Ensure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # Prepare config
+        config = {
+            "film_jid": self.film_jid,
+            "substrate_jid": self.substrate_jid,
+            "film_index": self.film_index,
+            "substrate_index": self.substrate_index,
+            "disp_intvl": 0.05,
+            "calculator_method": self.calculator_type.lower(),
+        }
+
+        config_filename = os.path.join(
+            self.output_dir,
+            f"config_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.json"
+        )
+
+        # Save config file
+        save_dict_to_json(config, config_filename)
+        self.log(f"Config file created: {config_filename}")
+
+        # Run intermat script using subprocess in self.output_dir
+        command = f"run_intermat.py --config_file {os.path.basename(config_filename)}"
+        self.log(f"Running command: {command}")
+
+        try:
+            result = subprocess.run(
+                command,
+                shell=True,
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=self.output_dir  # Set the working directory for the subprocess
+            )
+            self.log(f"Command output: {result.stdout}")
+        except subprocess.CalledProcessError as e:
+            self.log(f"Command failed with error: {e.stderr}")
+            return
+
+        # After execution, check for outputs in self.output_dir
+        main_results_filename = os.path.join(self.output_dir, "intermat_results.json")
+        if not os.path.exists(main_results_filename):
+            self.log(f"Results file not found: {main_results_filename}")
+            return
+
+        res = load_dict_from_json(main_results_filename)
+        w_adhesion = res.get("wads", [])
+        systems_info = res.get("systems", {})
+
+        # Handle intmat.png
+        intmat_filename = os.path.join(self.output_dir, "intmat.png")
+        if os.path.exists(intmat_filename):
+            new_intmat_filename = os.path.join(
+                self.output_dir,
+                f"intmat_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}.png"
+            )
+            os.rename(intmat_filename, new_intmat_filename)
+            self.job_info["intmat_plot"] = new_intmat_filename
+            self.log(f"intmat.png saved as {new_intmat_filename}")
+        else:
+            self.log("intmat.png not found.")
+
+        if "wads" in res:
+            # Save additional plots or data as needed
+            self.job_info["interface_scan_results"] = main_results_filename
+            self.job_info["w_adhesion"] = w_adhesion
+            self.job_info["systems_info"] = systems_info
+            self.log(f"Interface scan results saved to {main_results_filename}")
+            self.log(f"w_adhesion: {w_adhesion}")
+            self.log(f"systems_info: {systems_info}")
+            save_dict_to_json(self.job_info, self.get_job_info_filename())
+        else:
+            self.log(f"No 'wads' key in results file: {main_results_filename}")
+
+    def get_job_info_filename(self):
+        if hasattr(self, 'jid') and self.jid:
+            return os.path.join(
+                self.output_dir, f"{self.jid}_{self.calculator_type}_job_info.json"
+            )
+        else:
+            return os.path.join(
+                self.output_dir, f"Interface_{self.film_jid}_{self.film_index}_{self.substrate_jid}_{self.substrate_index}_{self.calculator_type}_job_info.json"
+            )
+
+    import numpy as np
+    import pandas as pd
+    from sklearn.metrics import mean_absolute_error, r2_score
+    import matplotlib.pyplot as plt
+    from matplotlib.gridspec import GridSpec
+    import os
+
+    def run_all(self):
+        """Run selected analyses based on configuration."""
+        # Start timing the entire run
+        start_time = time.time()
+        if self.use_conventional_cell:
+            self.log("Using conventional cell for analysis.")
+            self.atoms = self.atoms.get_conventional_atoms
+        else:
+            self.atoms = self.atoms
+        # Relax the structure if specified
+        if 'relax_structure' in self.properties_to_calculate:
+            relaxed_atoms = self.relax_structure()
+        else:
+            relaxed_atoms = self.atoms
+
+        # Proceed only if the structure is relaxed or original atoms are used
+        if relaxed_atoms is None:
+            self.log("Relaxation did not converge. Exiting.")
+            return
+
+        if 'calculate_forces' in self.properties_to_calculate:
+            self.calculate_forces(self.atoms)
+
+        # Lattice parameters before and after relaxation
+        lattice_initial = self.atoms.lattice
+        lattice_final = relaxed_atoms.lattice
+
+        # Prepare final results dictionary
+        final_results = {}
+
+        # Initialize variables for error calculation
+        err_a = err_b = err_c = err_vol = err_form = err_kv = err_c11 = err_c44 = err_surf_en = err_vac_en = np.nan
+        form_en_entry = kv_entry = c11_entry = c44_entry = 0
+
+        # Calculate E-V curve and bulk modulus if specified
+        if 'calculate_ev_curve' in self.properties_to_calculate:
+            _, _, _, _, bulk_modulus, _, _ = self.calculate_ev_curve(relaxed_atoms)
+            kv_entry = self.reference_data.get("bulk_modulus_kv", 0)
+            final_results['modulus'] = {
+                'kv': bulk_modulus,
+                'kv_entry': kv_entry
+            }
+            err_kv = mean_absolute_error([kv_entry], [bulk_modulus]) if bulk_modulus is not None else np.nan
+
+                
+        # Formation energy
+        if 'calculate_formation_energy' in self.properties_to_calculate:
+            formation_energy = self.calculate_formation_energy(relaxed_atoms)
+            form_en_entry = self.reference_data.get("formation_energy_peratom", 0)
+            final_results['form_en'] = {
+                'form_energy': formation_energy,
+                'form_energy_entry': form_en_entry
+            }
+            err_form = mean_absolute_error([form_en_entry], [formation_energy])
+
+        # Elastic tensor
+        if 'calculate_elastic_tensor' in self.properties_to_calculate:
+            elastic_tensor = self.calculate_elastic_tensor(relaxed_atoms)
+            c11_entry = self.reference_data.get("elastic_tensor", [[0]])[0][0]
+            c44_entry = self.reference_data.get("elastic_tensor", [[0, 0, 0, [0, 0, 0, 0]]])[3][3]
+            final_results['elastic_tensor'] = {
+                'c11': elastic_tensor.get("C_11", 0),
+                'c44': elastic_tensor.get("C_44", 0),
+                'c11_entry': c11_entry,
+                'c44_entry': c44_entry
+            }
+            err_c11 = mean_absolute_error([c11_entry], [elastic_tensor.get("C_11", np.nan)])
+            err_c44 = mean_absolute_error([c44_entry], [elastic_tensor.get("C_44", np.nan)])
+
+        # Phonon analysis
+        if 'run_phonon_analysis' in self.properties_to_calculate:
+            phonon, zpe = self.run_phonon_analysis(relaxed_atoms)
+            final_results['zpe'] = zpe
+        else:
+            zpe = None
+
+        # Surface energy analysis
+        if 'analyze_surfaces' in self.properties_to_calculate:
+            self.analyze_surfaces()
+            surf_en, surf_en_entry = [], []
+            surface_entries = get_surface_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+
+            indices_list = self.surface_settings.get('indices_list', [
+                [1, 0, 0],
+                [1, 1, 1],
+                [1, 1, 0],
+                [0, 1, 1],
+                [0, 0, 1],
+                [0, 1, 0],
+            ])
+
+            for indices in indices_list:
+                surface_name = f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}"
+                calculated_surface_energy = self.job_info.get(surface_name, 0)
+                try:
+                    # Try to match the surface entry
+                    matching_entry = next(
+                        (entry for entry in surface_entries if entry['name'].strip() == surface_name.strip()),
+                        None
+                    )
+                    if matching_entry and calculated_surface_energy != 0 and matching_entry["surf_en_entry"] != 0:
+                        surf_en.append(calculated_surface_energy)
+                        surf_en_entry.append(matching_entry["surf_en_entry"])
+                    else:
+                        print(f"No valid matching entry found for {surface_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing surface {surface_name}: {e}")
+                    self.log(f"Error processing surface {surface_name}: {str(e)}")
+                    continue  # Skip this surface and move to the next one
+            final_results['surface_energy'] = [
+                {
+                    "name": f"Surface-{self.jid}_miller_{'_'.join(map(str, indices))}",
+                    "surf_en": se,
+                    "surf_en_entry": see
+                }
+                for se, see, indices in zip(surf_en, surf_en_entry, indices_list)
+            ]
+            err_surf_en = mean_absolute_error(surf_en_entry, surf_en) if surf_en else np.nan
+
+        # Vacancy energy analysis
+        if 'analyze_defects' in self.properties_to_calculate:
+            self.analyze_defects()
+            vac_en, vac_en_entry = [], []
+            vacancy_entries = get_vacancy_energy_entry(self.jid, collect_data(dft_3d, vacancydb, surface_data))
+            for defect in Vacancy(self.atoms).generate_defects(on_conventional_cell=True, enforce_c_size=8, extend=1):
+                defect_name = f"{self.jid}_{defect.to_dict()['symbol']}"
+                vacancy_energy = self.job_info.get(f"vacancy_formation_energy for {defect_name}", 0)
+                try:
+                    # Try to match the vacancy entry
+                    matching_entry = next(
+                        (entry for entry in vacancy_entries if entry['symbol'] == defect_name),
+                        None
+                    )
+                    if matching_entry and vacancy_energy != 0 and matching_entry['vac_en_entry'] != 0:
+                        vac_en.append(vacancy_energy)
+                        vac_en_entry.append(matching_entry['vac_en_entry'])
+                    else:
+                        print(f"No valid matching entry found for {defect_name}")
+                except Exception as e:
+                    # Handle the exception, log it, and continue
+                    print(f"Error processing defect {defect_name}: {e}")
+                    self.log(f"Error processing defect {defect_name}: {str(e)}")
+                    continue  # Skip this defect and move to the next one
+            final_results['vacancy_energy'] = [
+                {"name": ve_name, "vac_en": ve, "vac_en_entry": vee}
+                for ve_name, ve, vee in zip(
+                    [f"{self.jid}_{defect.to_dict()['symbol']}" for defect in Vacancy(self.atoms).generate_defects(
+                        on_conventional_cell=True, enforce_c_size=8, extend=1
+                    )],
+                    vac_en,
+                    vac_en_entry
+                )
+            ]
+            err_vac_en = mean_absolute_error(vac_en_entry, vac_en) if vac_en else np.nan
+
+        # Additional analyses
+        if 'analyze_interfaces' in self.properties_to_calculate and self.film_jid and self.substrate_jid:
+            self.analyze_interfaces()
+
+        if 'run_phonon3_analysis' in self.properties_to_calculate:
+            self.run_phonon3_analysis(relaxed_atoms)
+
+        if 'calculate_thermal_expansion' in self.properties_to_calculate:
+            self.calculate_thermal_expansion(relaxed_atoms)
+
+        if 'general_melter' in self.properties_to_calculate:
+            quenched_atoms = self.general_melter(relaxed_atoms)
+            if 'calculate_rdf' in self.properties_to_calculate:
+                self.calculate_rdf(quenched_atoms)
+
+        # Record lattice parameters
+        final_results['energy'] = {
+            "initial_a": lattice_initial.a,
+            "initial_b": lattice_initial.b,
+            "initial_c": lattice_initial.c,
+            "initial_vol": lattice_initial.volume,
+            "final_a": lattice_final.a,
+            "final_b": lattice_final.b,
+            "final_c": lattice_final.c,
+            "final_vol": lattice_final.volume,
+            "energy": self.job_info.get("final_energy_structure", 0)
+        }
+
+        # Error calculations
+        err_a = mean_absolute_error([lattice_initial.a], [lattice_final.a])
+        err_b = mean_absolute_error([lattice_initial.b], [lattice_final.b])
+        err_c = mean_absolute_error([lattice_initial.c], [lattice_final.c])
+        err_vol = mean_absolute_error([lattice_initial.volume], [lattice_final.volume])
+
+        # Create an error dictionary
+        error_dat = {
+            "err_a": err_a,
+            "err_b": err_b,
+            "err_c": err_c,
+            "err_form": err_form,
+            "err_vol": err_vol,
+            "err_kv": err_kv,
+            "err_c11": err_c11,
+            "err_c44": err_c44,
+            "err_surf_en": err_surf_en,
+            "err_vac_en": err_vac_en,
+            "time": time.time() - start_time
+        }
+
+        print("Error metrics calculated:", error_dat)
+
+        # Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+        # Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+        # Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        # Write results to a JSON file
+        output_file = os.path.join(self.output_dir, f"{self.jid}_{self.calculator_type}_results.json")
+        save_dict_to_json(final_results, output_file)
+
+        # Log total time
+        total_time = error_dat['time']
+        self.log(f"Total time for run: {total_time} seconds")
+
+        return error_dat
+
+
+# Create a DataFrame for error data
+        df = pd.DataFrame([error_dat])
+
+# Save the DataFrame to CSV
+        unique_dir = os.path.basename(self.output_dir)
+        fname = os.path.join(self.output_dir, f"{unique_dir}_error_dat.csv")
+        df.to_csv(fname, index=False)
+
+# Plot the scorecard with errors
+        self.plot_error_scorecard(df)
+
+        return error_dat
+
+    def plot_error_scorecard(self, df):
+        import plotly.express as px
+
+        fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+        unique_dir = os.path.basename(self.output_dir)
+        fname_plot = os.path.join(self.output_dir, f"{unique_dir}_error_scorecard.png")
+        fig.write_image(fname_plot)
+        fig.show()
+
+def analyze_multiple_structures(jid_list, calculator_types, chemical_potentials_file):
+    composite_error_data = {}
+
+    for calculator_type in calculator_types:
+        # List to store individual error DataFrames
+        error_dfs = []
+
+        for jid in jid_list:
+            print(f"Analyzing {jid} with {calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                jid=jid,
+                calculator_type=calculator_type,
+                chemical_potentials_file=chemical_potentials_file,
+            )
+            # Run analysis and get error data
+            error_dat = analyzer.run_all()
+            error_df = pd.DataFrame([error_dat])
+            error_dfs.append(error_df)
+
+        # Concatenate all error DataFrames
+        all_errors_df = pd.concat(error_dfs, ignore_index=True)
+
+        # Compute composite errors by ignoring NaN values
+        composite_error = all_errors_df.mean(skipna=True).to_dict()
+
+        # Store the composite error data for this calculator type
+        composite_error_data[calculator_type] = composite_error
+
+    # Once all materials and calculators have been processed, create a DataFrame
+    composite_df = pd.DataFrame(composite_error_data).transpose()
+
+    # Plot the composite scorecard
+    plot_composite_scorecard(composite_df)
+
+    # Save the composite dataframe
+    composite_df.to_csv("composite_error_data.csv", index=True)
+
+def analyze_multiple_interfaces(film_jid_list, substrate_jid_list, calculator_types, chemical_potentials_file, film_index="1_1_0", substrate_index="1_1_0"):
+    for calculator_type in calculator_types:
+        for film_jid in film_jid_list:
+            for substrate_jid in substrate_jid_list:
+                print(f"Analyzing interface between {film_jid} and {substrate_jid} with {calculator_type}...")
+                analyzer = MaterialsAnalyzer(
+                    calculator_type=calculator_type,
+                    chemical_potentials_file=chemical_potentials_file,
+                    film_jid=film_jid,
+                    substrate_jid=substrate_jid,
+                    film_index=film_index,
+                    substrate_index=substrate_index,
+                )
+                analyzer.analyze_interfaces()
+
+def plot_composite_scorecard(df):
+    """Plot the composite scorecard for all calculators"""
+    fig = px.imshow(df, text_auto=True, aspect="auto", labels=dict(color="Error"))
+    fig.update_layout(title="Composite Scorecard for Calculators")
+    
+    # Save plot
+    fname_plot = "composite_error_scorecard.png"
+    fig.write_image(fname_plot)
+    fig.show()
+
+
+class MLearnForcesAnalyzer:
+    def __init__(self, calculator_type, mlearn_elements, output_dir=None):
+        self.calculator_type = calculator_type
+        self.mlearn_elements = mlearn_elements
+        elements_str = "_".join(self.mlearn_elements)
+        self.output_dir = output_dir or f"mlearn_analysis_{elements_str}_{calculator_type}"
+        os.makedirs(self.output_dir, exist_ok=True)
+        self.log_file = os.path.join(self.output_dir, "mlearn_analysis_log.txt")
+        self.setup_logger()
+        self.calculator = self.setup_calculator()
+        self.job_info = {
+            "calculator_type": calculator_type,
+            "mlearn_elements": mlearn_elements,
+        }
+
+    def setup_logger(self):
+        import logging
+        self.logger = logging.getLogger("MLearnForcesAnalyzer")
+        self.logger.setLevel(logging.INFO)
+        fh = logging.FileHandler(self.log_file)
+        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+        fh.setFormatter(formatter)
+        self.logger.addHandler(fh)
+
+    def log(self, message):
+        self.logger.info(message)
+        print(message)
+
+    def setup_calculator(self):
+        return setup_calculator(self.calculator_type)
+
+    def run(self):
+        for element in self.mlearn_elements:
+            self.compare_mlearn_properties(element)
+
+    def compare_mlearn_properties(self, element):
+        """
+        Compare forces and stresses calculated by the FF calculator with mlearn DFT data for a given element.
+
+        Args:
+            element (str): Element symbol to filter structures (e.g., 'Si').
+        """
+        # Download the mlearn dataset if not already present
+        mlearn_zip_path = 'mlearn.json.zip'
+        if not os.path.isfile(mlearn_zip_path):
+            self.log("Downloading mlearn dataset...")
+            url = 'https://figshare.com/ndownloader/files/40357663'
+            response = requests.get(url)
+            with open(mlearn_zip_path, 'wb') as f:
+                f.write(response.content)
+            self.log("Download completed.")
+
+        # Read the JSON data from the zip file
+        with zipfile.ZipFile(mlearn_zip_path, 'r') as z:
+            with z.open('mlearn.json') as f:
+                mlearn_data = json.load(f)
+
+        # Convert mlearn data to DataFrame
+        df = pd.DataFrame(mlearn_data)
+
+        # Filter the dataset for the specified element
+        df['elements'] = df['atoms'].apply(lambda x: x['elements'])
+        df = df[df['elements'].apply(lambda x: element in x)]
+        df = df.reset_index(drop=True)
+        self.log(f"Filtered dataset to {len(df)} entries containing element '{element}'")
+
+        # Initialize lists to store results
+        force_results = []
+        stress_results = []
+
+        # Iterate over each structure
+        for idx, row in df.iterrows():
+            jid = row.get('jid', f"structure_{idx}")
+            atoms_dict = row['atoms']
+            atoms = Atoms.from_dict(atoms_dict)
+            dft_forces = np.array(row['forces'])
+            dft_stresses = np.array(row['stresses'])  # Original stresses in kBar
+
+            # Convert DFT stresses from kBar to GPa
+            dft_stresses_GPa = dft_stresses * -0.1  # kBar to GPa
+
+            # Convert DFT stresses to full 3x3 tensors
+            if dft_stresses_GPa.ndim == 1 and dft_stresses_GPa.size == 6:
+                dft_stress_tensor = voigt_6_to_full_3x3_stress(dft_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: DFT stresses not in expected format.")
+                continue  # Skip structures with unexpected stress format
+
+            # Calculate predicted properties
+            predicted_forces, predicted_stresses = self.calculate_properties(atoms)
+
+            # Convert predicted stresses from eV/ų to GPa
+            if predicted_stresses is not None and predicted_stresses.size == 6:
+                predicted_stresses_GPa = predicted_stresses * 160.21766208  # eV/ų to GPa
+                predicted_stress_tensor = voigt_6_to_full_3x3_stress(predicted_stresses_GPa)
+            else:
+                self.log(f"Skipping {jid}: Predicted stresses not available.")
+                continue  # Skip structures where stresses are not available
+
+            # Flatten the 3x3 stress tensors to 9-component arrays for comparison
+            dft_stress_flat = dft_stress_tensor.flatten()
+            predicted_stress_flat = predicted_stress_tensor.flatten()
+
+            # Store the results
+            force_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_forces.flatten())),
+                'prediction': ';'.join(map(str, predicted_forces.flatten())),
+            })
+            stress_results.append({
+                'id': jid,
+                'target': ';'.join(map(str, dft_stress_flat)),
+                'prediction': ';'.join(map(str, predicted_stress_flat)),
+            })
+
+            # Optional: Progress indicator
+            if idx % 10 == 0:
+                self.log(f"Processed {idx + 1}/{len(df)} structures.")
+
+        # Ensure we have data to process
+        if not force_results or not stress_results:
+            self.log("No valid data found for forces or stresses. Exiting.")
+            return
+
+        # Save results to CSV files
+        force_df = pd.DataFrame(force_results)
+        force_csv = os.path.join(self.output_dir, f'AI-MLFF-forces-mlearn_{element}-test-multimae.csv')
+        force_df.to_csv(force_csv, index=False)
+        self.log(f"Saved force comparison data to '{force_csv}'")
+
+        stress_df = pd.DataFrame(stress_results)
+        stress_csv = os.path.join(self.output_dir, f'AI-MLFF-stresses-mlearn_{element}-test-multimae.csv')
+        stress_df.to_csv(stress_csv, index=False)
+        self.log(f"Saved stress comparison data to '{stress_csv}'")
+
+        # Zip the CSV files
+        self.zip_file(force_csv)
+        self.zip_file(stress_csv)
+
+        # Calculate error metrics
+        # Forces MAE
+        target_forces = np.concatenate(force_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_forces = np.concatenate(force_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        forces_mae = mean_absolute_error(target_forces, pred_forces)
+        self.log(f"Forces MAE for element '{element}': {forces_mae:.6f} eV/Å")
+
+        # Stresses MAE
+        target_stresses = np.concatenate(stress_df['target'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        pred_stresses = np.concatenate(stress_df['prediction'].apply(lambda x: np.array(x.split(';'), dtype=float)).values)
+        stresses_mae = mean_absolute_error(target_stresses, pred_stresses)
+        self.log(f"Stresses MAE for element '{element}': {stresses_mae:.6f} GPa")
+
+        # Save MAE to job_info
+        self.job_info[f'forces_mae_{element}'] = forces_mae
+        self.job_info[f'stresses_mae_{element}'] = stresses_mae
+        self.save_job_info()
+
+        # Plot parity plots
+        forces_plot_filename = os.path.join(self.output_dir, f'forces_parity_plot_{element}.png')
+        self.plot_parity(target_forces, pred_forces, 'Forces', 'eV/Å', forces_plot_filename, element)
+
+        stresses_plot_filename = os.path.join(self.output_dir, f'stresses_parity_plot_{element}.png')
+        self.plot_parity(target_stresses, pred_stresses, 'Stresses', 'GPa', stresses_plot_filename, element)
+
+    def calculate_properties(self, atoms):
+        """
+        Calculate forces and stresses on the given atoms.
+
+        Returns:
+            Tuple of forces and stresses.
+        """
+        # Convert atoms to ASE format and assign the calculator
+        ase_atoms = atoms.ase_converter()
+        ase_atoms.calc = self.calculator
+
+        # Calculate properties
+        forces = ase_atoms.get_forces()
+        stresses = ase_atoms.get_stress()  # Voigt 6-component stress
+
+        return forces, stresses  # Return forces and stresses in Voigt notation
+
+    def plot_parity(self, target, prediction, property_name, units, filename, element):
+        """
+        Plot parity plot for a given property.
+
+        Args:
+            target (array-like): Target values.
+            prediction (array-like): Predicted values.
+            property_name (str): Name of the property (e.g., 'Forces').
+            units (str): Units of the property (e.g., 'eV/Å' or 'GPa').
+            filename (str): Filename to save the plot.
+            element (str): Element symbol.
+        """
+        plt.figure(figsize=(8, 8), dpi=300)
+        plt.scatter(target, prediction, alpha=0.5, edgecolors='k', s=20)
+        min_val = min(np.min(target), np.min(prediction))
+        max_val = max(np.max(target), np.max(prediction))
+        plt.plot([min_val, max_val], [min_val, max_val], 'k--', lw=2)
+        plt.xlabel(f'Target {property_name} ({units})', fontsize=14)
+        plt.ylabel(f'Predicted {property_name} ({units})', fontsize=14)
+        plt.title(f'Parity Plot for {property_name} - Element {element}', fontsize=16)
+        plt.grid(True)
+        plt.tight_layout()
+        plt.savefig(filename)
+        plt.close()
+        self.log(f"Saved parity plot for {property_name} as '{filename}'")
+
+    def zip_file(self, filename):
+        zip_filename = filename + '.zip'
+        with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
+            zf.write(filename, arcname=os.path.basename(filename))
+        os.remove(filename)  # Remove the original file
+        self.log(f"Zipped data to '{zip_filename}'")
+
+    def save_job_info(self):
+        job_info_filename = os.path.join(self.output_dir, f"mlearn_{self.calculator_type}_job_info.json")
+        with open(job_info_filename, 'w') as f:
+            json.dump(self.job_info, f, indent=4)
+
+
+#jid_list=['JVASP-1002']
+jid_list_all = [ 'JVASP-1002', 'JVASP-816', 'JVASP-867', 'JVASP-1029', 'JVASP-861','JVASP-30', 'JVASP-8169', 'JVASP-890', 'JVASP-8158','JVASP-8118',
+    'JVASP-107', 'JVASP-39', 'JVASP-7844', 'JVASP-35106', 'JVASP-1174',
+    'JVASP-1372', 'JVASP-91', 'JVASP-1186', 'JVASP-1408', 'JVASP-105410',
+    'JVASP-1177', 'JVASP-79204', 'JVASP-1393', 'JVASP-1312', 'JVASP-1327',
+    'JVASP-1183', 'JVASP-1192', 'JVASP-8003', 'JVASP-96', 'JVASP-1198',
+    'JVASP-1195', 'JVASP-9147', 'JVASP-41', 'JVASP-34674', 'JVASP-113',
+    'JVASP-32', 'JVASP-840', 'JVASP-21195', 'JVASP-981', 'JVASP-969',
+    'JVASP-802', 'JVASP-943', 'JVASP-14812', 'JVASP-984', 'JVASP-972',
+    'JVASP-958', 'JVASP-901', 'JVASP-1702', 'JVASP-931', 'JVASP-963',
+    'JVASP-95', 'JVASP-1201', 'JVASP-14837', 'JVASP-825', 'JVASP-966',
+    'JVASP-993', 'JVASP-23', 'JVASP-828', 'JVASP-1189', 'JVASP-810',
+    'JVASP-7630', 'JVASP-819', 'JVASP-1180', 'JVASP-837', 'JVASP-919',
+    'JVASP-7762', 'JVASP-934', 'JVASP-858', 'JVASP-895']
+#calculator_types = ["alignn_ff_aff307k_lmdb_param_low_rad_use_force_mult_mp_tak4","alignn_ff_v5.27.2024","alignn_ff_aff307k_kNN_2_2_128"]
+
+if __name__ == "__main__":
+    import pprint
+    parser = argparse.ArgumentParser(description="Run Materials Analyzer")
+    parser.add_argument("--input_file", default="input.json", type=str, help="Path to the input configuration JSON file")
+    args = parser.parse_args()
+
+    input_file = loadjson(args.input_file)
+    input_file_data = CHIPSFFConfig(**input_file)
+    pprint.pprint(input_file_data.dict())
+
+    # If film_id is provided, treat it as a list
+    film_jids = input_file_data.film_id if input_file_data.film_id else []
+
+    # If substrate_id is provided, treat it as a list
+    substrate_jids = input_file_data.substrate_id if input_file_data.substrate_id else []
+
+    # Case 1: Interface calculations with film_jid and substrate_jid
+    if film_jids and substrate_jids:
+        # Loop through all film and substrate JIDs and perform interface analysis
+        for film_jid, substrate_jid in zip(film_jids, substrate_jids):
+            print(f"Analyzing interface between {film_jid} and {substrate_jid} with {input_file_data.calculator_type}...")
+            analyzer = MaterialsAnalyzer(
+                calculator_type=input_file_data.calculator_type,
+                chemical_potentials_file=input_file_data.chemical_potentials_file,
+                film_jid=film_jid,
+                substrate_jid=substrate_jid,
+                film_index=input_file_data.film_index,
+                substrate_index=input_file_data.substrate_index,
+                bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+                phonon_settings=input_file_data.phonon_settings,
+                properties_to_calculate=input_file_data.properties_to_calculate,
+            )
+            analyzer.analyze_interfaces()
+
+    # Case 2: Single JID provided
+    elif input_file_data.jid and input_file_data.calculator_type:
+        print(f"Analyzing {input_file_data.jid} with {input_file_data.calculator_type}...")
+        analyzer = MaterialsAnalyzer(
+            jid=input_file_data.jid,
+            calculator_type=input_file_data.calculator_type,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+        analyzer.run_all()
+
+    # Case 3: Multiple JIDs and calculator types provided (batch processing)
+    elif input_file_data.jid_list and input_file_data.calculator_types:
+        analyze_multiple_structures(
+            jid_list=input_file_data.jid_list,
+            calculator_types=input_file_data.calculator_types,
+            chemical_potentials_file=input_file_data.chemical_potentials_file,
+            bulk_relaxation_settings=input_file_data.bulk_relaxation_settings,
+            phonon_settings=input_file_data.phonon_settings,
+            properties_to_calculate=input_file_data.properties_to_calculate,
+            use_conventional_cell=input_file_data.use_conventional_cell,
+            surface_settings=input_file_data.surface_settings,
+            defect_settings=input_file_data.defect_settings,
+            phonon3_settings=input_file_data.phonon3_settings,
+            md_settings=input_file_data.md_settings
+        )
+
+    elif input_file_data.mlearn_elements and input_file_data.calculator_type:
+        print(f"Running mlearn forces comparison for elements {input_file_data.mlearn_elements} with {input_file_data.calculator_type}...")
+        mlearn_analyzer = MLearnForcesAnalyzer(
+            calculator_type=input_file_data.calculator_type,
+            mlearn_elements=input_file_data.mlearn_elements
+        )
+        mlearn_analyzer.run()
+    else:
+        print("Please provide valid arguments in the configuration file.")
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/AI-MLFF-forces-mlearnall_Si-test-multimae.csv.zip b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Si-test-multimae.csv.zip
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/AI-MLFF-forces-mlearnall_Si-test-multimae.csv.zip
rename to jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/AI-MLFF-forces-mlearnall_Si-test-multimae.csv.zip
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/metadata.json b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/metadata.json
new file mode 100644
index 000000000..93f5c9fd0
--- /dev/null
+++ b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/metadata.json
@@ -0,0 +1,15 @@
+{
+    "model_name": "CHIPS-FF",
+    "project_url": "https://github.com/usnistgov/chipsff",
+    "date_submitted": "11-22-2024",
+    "author_email": "daniel.wines@nist.gov",
+    "time_taken_seconds": {},
+    "team_name": "JARVIS",
+    "language": "python",
+    "os": "linux",
+    "software_used": "jarvis-tools,numpy,scipy,torch,alignn",
+    "hardware_used": "mr-french at NIST",
+    "git_url": [
+        "https://github.com/usnistgov/chipsff"
+    ]
+}
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/mlearn_alignn_ff_job_info.json b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/mlearn_alignn_ff_job_info.json
rename to jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/mlearn_alignn_ff_job_info.json
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/mlearn_analysis_log.txt b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/mlearn_analysis_log.txt
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/mlearn_analysis_log.txt
rename to jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/mlearn_analysis_log.txt
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/run.sh b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/run.sh
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/run.sh
rename to jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/run.sh
diff --git a/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/run_chipsff.py b/jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/run_chipsff.py
similarity index 100%
rename from jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff/run_chipsff.py
rename to jarvis_leaderboard/contributions/mlearn_analysis_Si_alignn_ff_5_27_24/run_chipsff.py
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diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 36e8c42300322a368adca4b6d0451a810ee80865..2a76ba288ef5b3207fc03900feb08b2f37e25908 100644
GIT binary patch
delta 882
zcmV-&1C9LH1^WjWP)h>@6aWAK2mqr}gIV5h7Q!b30015ckr+UKRk;o&MGVaMTZWPE
zBP1n4LgEKlf=vJ+{$G@5cib<v+DBE{)!)8e|NQ&s*KdFP_wDb$zWnn0&%gcp6Eo>$
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delta 589
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bS$0`kmf|7=0ssIN1(TQqT?ShM00000q8JmC

diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index b622c2016c5b63660009ac083c9a74d9653a5836..ee1fc5a3dc459e8e71d46863c38616fc8e1261c7 100644
GIT binary patch
delta 876
zcmV-y1C#v31@s3PP)h>@6aWAK2mqr}gIVfQ9FZUc008m_kr+RJRaH9<B}EL(Im<Be
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zfY=`u{@IS5EeL@a`wvh{0Rj{Q6aWAK2mqr}gIVfQ9FZUc008m_lVJi~26qDh0000;
C)R+GN

delta 570
zcmV-A0>%CG2gC&!P)h>@6aWAK2mpauc3BCfPY3V;007|xkr+RJO;kH>TtN)$bC#EZ
z{+}|fD+2~{fjGrBNnrT?i1e_dakn5e6h-pw<Nf;g@%#Pp>+|vV&$pMKKi*!yr(LqZ
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I0ssI20HBux(f|Me

diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 8a5852b3a8eb39143f03a4ccafd20497a38bb60c..60e2451503a0efb54aef219e425db0b176edbf3c 100644
GIT binary patch
delta 1479
zcmV;&1vvW52+j-`P)h>@6aWAK2mqr}gIVej@+fu%008|9kr+XLRhGN1Y_|=Ddq0aJ
zkd$~U)4B=}z%LM|IE@_yd4J^7*h_0%?6c>Lwj}E3f8mc`$AA9&?_YoX>wiCf|Lv!r
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h000080HacaS?Un-D0T$^0R0P-=L25`&jkPg005BL)?NSr

delta 961
zcmV;y13vuD49o}^P)h>@6aWAK2mpdvc3C%xFG6$!004&vkr+XLO;x#$RXY&O`IdfG
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j0~7!N00;nrS$0`Bi7!HQ0{{Sr2b1XqUk1$s00000G%muH

diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index d37f98f08b7873427b882e6f1ece4f3fc62e73b7..50b21ca6506ea5fb93cc064bcd9d5bd8c5192759 100644
GIT binary patch
delta 1510
zcmV<C1sVG22<{9RP)h>@6aWAK2mqr}gISGvGzOLh008?8kr+aMm%FZIw+)7SKg&1}
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z?I<5t>M_ci!j;v-?kO9S_{;@=bvEe<Wo>m5x3A|MuK-erKM?-QR}3OQwybRh%sTWA
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M1YZX41poj507ppcYXATM

delta 988
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diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index b7a7ad6f79c1c4b027ddf8448d651615db0d8692..178fa16eba2888d9eb67f25da4f4a4f13f33f627 100644
GIT binary patch
delta 1519
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delta 994
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diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index d38f6b8f54e7caf26152281444f42848084cad64..fa9c0e66ac175a2cf5394700af4e2d23fc287f8c 100644
GIT binary patch
delta 1476
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delta 961
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diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip
index 88f1238ae6f586b1cbb3f1dc367bae1e56a96ccb..4c51431f1b5b943fa869143b64baffb154bbf7d3 100644
GIT binary patch
delta 1357
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delta 1759
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diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip
new file mode 100644
index 0000000000000000000000000000000000000000..b2301850640238297acc63e347946b0766fae663
GIT binary patch
literal 857
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diff --git a/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/orb-v2/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip
index 3168ef266132ba36c13330ed55825cd0daca6032..d863982edcf4a5e7b6d1b4f3badd433a36b1954a 100644
GIT binary patch
delta 1480
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delta 960
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diff --git a/jarvis_leaderboard/contributions/qmcpack_dmc_pbe/.DS_Store b/jarvis_leaderboard/contributions/qmcpack_dmc_pbe/.DS_Store
deleted file mode 100644
index fe5945733bb7850cfff3866dd25329a2e410037b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/jarvis_leaderboard/contributions/sevennet/.DS_Store b/jarvis_leaderboard/contributions/sevennet/.DS_Store
deleted file mode 100644
index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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literal 6148
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-a-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-a-dft_3d_chipsff-test-mae.csv.zip
index 389a053c6de995026806c73da2d60f07400d6271..ebd8406032bcf0f58b30e9f593c209b0b706cf5f 100644
GIT binary patch
delta 893
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delta 588
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-b-dft_3d_chipsff-test-mae.csv.zip
index 57870206bb079d1c0245b25ee4df4cdd3f56cd2e..970597da326837b0cbb61a80d7691965fd6803a9 100644
GIT binary patch
delta 886
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delta 593
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-c-dft_3d_chipsff-test-mae.csv.zip
index efd0ef90c920bf6a9eb112c41028f37280abdb68..43c4901b0d5c3c460cb46148cb920e2b2edb248f 100644
GIT binary patch
delta 873
zcmV-v1D5>51?>kLP)h>@6aWAK2mqr}gIVAPN_-gu008j^0023YaRNMlRaH5TC^Znx
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delta 569
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-c11-dft_3d_chipsff-test-mae.csv.zip
index 6e17437b24e0c6766f99312ef2b3fadf0d6f711f..ee6af9225f1c83c24538a78c7a3f6fc1b670911b 100644
GIT binary patch
delta 1476
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delta 970
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-c44-dft_3d_chipsff-test-mae.csv.zip
index 9353e902515ced5c2b8344a97dcc7cde4a88ba26..1425e2ecd06f9dcecd6a2ac0b7f363826b55f514 100644
GIT binary patch
delta 1492
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delta 968
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-form_en-dft_3d_chipsff-test-mae.csv.zip
index 07d55f2787f7361991e248e8320e559a830539f8..1505fecfdbae72a101b310930236ee2efe81f225 100644
GIT binary patch
delta 1510
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delta 1020
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-kv-dft_3d_chipsff-test-mae.csv.zip
index c41e0361e99bf02af3ba897b0653c77118b5a82a..5b7cfd545f939f1088832b87ab93fbbd173d78da 100644
GIT binary patch
delta 1485
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delta 969
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-surf_en-dft_3d_chipsff-test-mae.csv.zip
index 03d2da7d0619e304eff19c426d0635aa23f8fd52..d032101f3e48071c52c6702a9ec84d24b1557284 100644
GIT binary patch
delta 1339
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-vac_en-dft_3d_chipsff-test-mae.csv.zip
new file mode 100644
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diff --git a/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip b/jarvis_leaderboard/contributions/sevennet/AI-SinglePropertyPrediction-vol-dft_3d_chipsff-test-mae.csv.zip
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