Adapt Makers to use pydantic models instead of yamls

[naik-aakash]

Closes #305 , #329, #324 and #330

Changes

• providing default config file arg seems redundant (Maybe remove)
• check if MLIPFitMaker also needs this change
• Add type checks before updating kwargs of config (use pydantic to achieve this automatically)
• add test to ensure RssConfig custom model work as intended
• add MLIP hypers pydantic models
• use pydantic models instead of load yamls for defaults (adapt fitting jobs / flows)
• Move database_dir arg back to Make see RssMaker does not work as expected #329 (comment)
• Extend MLIP hyper parameter models where possible (Maybe MACE for now > also no typechecking for fine-tuning hypers for MACE at this point in models)
• remove use defaults arg of MLIPFitMaker/ machine learning fit method
• Check if nequip_fitting function can be cleaned up
• Add hypeparameters arg to machine_learning_fit function
• Think if value Error should be raised or a warning when unexpected kwarg gets passed for fits. (Currently I decided to raise a warning)
• Extend M3GNET and NEQUIP hyperparameters
• Enable fine-tuning of M3GNET models
• Figure out missing args in RssMaker doc-strings and placement of args (need some input from @YuanbinLiu)
• Update fine-tuning docs for MACE
• Update RssMaker documentation to reflect changes
• Update AIRSS install instructions
• Changed RssConfig yaml file format to enable typechecking of mlip hyperparameters
• Update config file example to also include defaults which have none for formatting clarity
• Add a note to upgrade pymongo if using fireworks in documentation

[JaGeo]

Shouldn't we rather do something similar to atomate2 settings?

[JaGeo]

Or this approach? https://github.com/materialsproject/pymatgen/blob/da607e86f9ce8aec942067c6c1a4fda6e04915dd/src/pymatgen/io/vasp/sets.py#L86

[naik-aakash]

Hi @JaGeo , i think both above approach we can use to load the default yamls, which I plan to do next : similar to atomate2 settings

But we would still need postinit in the maker if we expect user to provide modified yaml path (so it gets read when the flow is created) and we don't want to copy that yamls to remote cluster. Due to nature of jobflow delayed execution

[JaGeo]

@naik-aakash okay! Likely pymatgen approach is th closest to what we want for defaults

[JaGeo]

Another thought: post init will not work if you initialize the flow during run time

[JaGeo]

@naik-aakash Great. Yes, please go ahead. And thanks @YuanbinLiu and @nfragapane !!

[YuanbinLiu]

Should we keep the line as well?

[naik-aakash]

This will not work as output reference cannot be resolved outside a job but rather inside. Have now changed it in the code and it works fine now

[naik-aakash]

This change was needed which fixes all the issues

https://github.com/naik-aakash/autoplex/blob/71cb7b42eb2fa45e6fcb01660e8fc015bdd04010/src/autoplex/data/rss/utils.py#L398

[JaGeo]

@naik-aakash thanks! Some of the fixes were due to my change allowing to finetue on another system than one does DFT