Article(id=1319222554045297294, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220835, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1665676800000, receivedDateStr=2022-10-14, revisedDate=1668268800000, revisedDateStr=2022-11-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456766853, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456766853, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456766853, creator=13701087609, updateTime=1792456766853, updator=13701087609, issue=Issue{id=1319222524555153701, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='2', pageStart='283', pageEnd='560', issueExtLink='null', onlineDate='null', pubDate='1676822400000', pubDateStr='2023-02-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1792456759823, creator='13701087609', updateTime=1792456885477, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319223051636560419, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319223051636560420, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=552, endPage=560, ext={EN=ArticleExt(id=1319222555861430927, articleId=1319222554045297294, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Review on Machine Learning Accelerated Crystal Structure Prediction, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

Crystal structure prediction is a powerful theoretical simulation tool, which can determine the crystal structure of materials with the given information of chemical composition. However, its application is severely limited due to the highly computational cost. In recent years, the state-of-art machine learning methods reveal a promising prospect in accelerating the conventional scientific computing, thus introducing the methods into the crystal structure prediction. This review briefly introduced recent progress on the application of machine learning for the crystal structure prediction. Two aspects were discussed, i.e., accelerating the energy evaluation and enhancing the potential energy surface sampling. In addition, some insights into the future development in this aspect were also suggested.

, authors=Xiaoshan LUO^{1, 2}, Zhenyu WANG^{2, 3}, Pengyue GAO^{1, 2}, Wei ZHANG², Jian LV^{1, 2}, Yanchao WANG^{1, 2}, authorsList=Xiaoshan LUO, Zhenyu WANG, Pengyue GAO, Wei ZHANG, Jian LV, Yanchao WANG, authorCompany=null, correspAuthors=Wei ZHANG, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319222556155032208, articleId=1319222554045297294, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=机器学习加速理论晶体结构预测研究进展, columnId=1321891389344953005, journalTitle=硅酸盐学报, columnName=机器学习在材料研发中的应用专题—综合评述, runingTitle=null, highlight=null, articleAbstract=

理论晶体结构预测可以在给定化学组分的条件下确定材料的晶体结构，已成为材料科学研究的重要工具。然而，该方法一直面临计算成本高的瓶颈问题。近年来，新兴机器学习方法在传统科学计算上展现了广阔的应用前景，逐渐被引入到晶体结构预测领域。本文主要讨论机器学习方法在理论晶体结构预测领域的最新研究进展，分别从加速晶体结构的能量计算和势能面的探索两个方面介绍领域的最新成果，并对未来研究可能的发展提出抛砖引玉的见解。

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LUO Xiaoshan (1998-), male, Doctoral candidate. Email: luoxs21@mails.jlu.edu.cn

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LUO Xiaoshan (1998-), male, Doctoral candidate. Email: luoxs21@mails.jlu.edu.cn

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罗啸山（1998—），男，博士研究生。

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罗啸山（1998—），男，博士研究生。

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