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research.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
<meta charset="utf-8">
<title>MTC Srolovitz Group</title>
<meta name="viewport" content="width=device-width,
initial-scale=1.0">
<meta name="description" content="">
<meta name="author" content="http://webthemez.com">
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<link href="css/bootstrap.min.css" rel="stylesheet">
<link href="css/fancybox/jquery.fancybox.css" rel="stylesheet">
<link href="css/jcarousel.css" rel="stylesheet">
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<body>
<div id="wrapper">
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<header>
<div class="navbar navbar-default navbar-static-top">
<div class="container">
<div class="navbar-header"> <button type="button"
class="navbar-toggle" data-toggle="collapse"
data-target=".navbar-collapse"> <span class="icon-bar"></span>
<span class="icon-bar"></span> <span class="icon-bar"></span> </button>
</div>
<div class="navbar-collapse collapse ">
<ul class="nav navbar-nav">
<li><a href="index.html"> <font size="+1"><b>Home</b></font> </a></li>
<li class="active"><a href="joinus.html"> <font size="+1"><b>Research</b></font> </a></li>
<li><a href="team.html"> <font size="+1"><b>Our Team</b></font> </a></li>
<li><a href="joinus.html"> <font size="+1"><b>Join Us</b></font> </a></li>
</ul>
</div>
<table border="0" cellspacing="2" cellpadding="2"
height="94" width="766">
<tbody>
<tr>
<td valign="top"><img src="img/HKU_logo.jpg"
moz-do-not-send="true" alt="HKU Logo" height="92"
width="81"> <br>
</td>
<td valign="center"><font size="+3"><b>David J. Srolovitz</b></font><br>
<span class="clear spacer_responsive_hide_mobile " style="height:5px;display:block;"></span> <font size="+2"><b> Materials Theory and Computation Group</b></font><br>
<span class="clear spacer_responsive_hide_mobile " style="height:5px;display:block;"></span> <font size="+2"><b> The University of Hong Kong</b></font><br>
</td>
</tr>
</tbody>
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<span class="clear spacer_responsive_hide_mobile" style="height:30px;display:block;"></span>
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<section class="callaction">
<div class="container">
<div class="row">
<div class="col-md-12">
<div>
<!-- BEGIN GRAIN BOUNDARY MODELS -->
<h3>Grain boundary models</h3>
<p> To be added.
</p>
<hr>
<!-- END GRAIN BOUNDARY MODELS -->
<!-- BEGIN MLP -->
<h3>Machine learning potentials</h3>
<p> Machine Learning (ML) is well known for its successes in many fields, including in materials science. Among these applications, machine learning-based interatomic potentials have emerged as a new approach to achieve a balance between accuracy, generality, and efficiency in describing the energy and forces associated with different atomic structures in atomistic simulations. The machine-learning, Deep Potential (DP) method, is a relatively new and rapidly developing approach that has recently been applied to a wide range of materials systems. A particularly important application of atomistic simulation of materials is for defect structure and properties, since many important mechanical, electric, optical, etc. properties are determined by defects and their arrangements. We are collaborating with DP developers in Beijing and applying ML DPs for point defects, dislocation, and twinning properties of metals and alloys. Future opportunities lie in DPs for multi-component alloys and the expansion of DPs to a much broader range of materials. <br>
<br>
References:<br>
[1] Tongqi Wen, Linfeng Zhang, Han Wang, Weinan E, David J. Srolovitz, Deep potentials for materials science, <a href="http://doi.org/10.1088/2752-5724/ac681d" target="_blank">Materials Futures 1, 022601 (2022)</a>. <br>
[2] Tongqi Wen, Rui Wang, Lingyu Zhu, Linfeng Zhang, Han Wang, David J. Srolovitz, Zhaoxuan Wu, Specialising neural network potentials for accurate properties and application to the mechanical response of titanium, <a href="http://doi.org/10.1038/s41524-021-00661-y" target="_black">npj Computational Materials 7, 206 (2021)</a>. <br>
[3] Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J. Srolovitz, Tongqi Wen, Zhaoxuan Wu, Classical and Machine Learning Interatomic Potentials for BCC Vanadium, <a href="https://arxiv.org/abs/2209.12322" target="_blank"> arXiv:2209.12322 (2022)</a>. <br>
</p>
<hr>
<!-- END MLP -->
<!-- BEGIN ATOMISTIC SIMULATIONS OF DEFECTS -->
<h3>Stress induced phase transformations and allotropy in ultra-strength materials</h3>
<p> To be added.
</p>
<hr>
<!-- END ATOMISTIC SIMULATIONS OF DEFECTS -->
<!-- BEGIN ATOMISTIC SIMULATIONS OF DEFECTS -->
<h3>Atomistic simulations of defect properties</h3>
<p> To be added.
</p>
<hr>
<!-- END ATOMISTIC SIMULATIONS OF DEFECTS -->
<!-- BEGIN AI driven discovery of HEA -->
<h3>AI driven discovery of HEA</h3>
<p> To be added.
</p>
<hr>
<!-- END AI driven discovery of HEA -->
</div>
</div>
</div>
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</section>
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