Article(id=1319222576644215552, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220923, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666713600000, receivedDateStr=2022-10-26, revisedDate=1667923200000, revisedDateStr=2022-11-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456772242, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456772242, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456772242, creator=13701087609, updateTime=1792456772242, 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=510, endPage=519, ext={EN=ArticleExt(id=1319222578321937153, articleId=1319222576644215552, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Application of Machine-Learning Assisted Dynamics Simulations in Nano-Scale Catalysis, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

As one of the important simulation methods in computational catalysis, molecular dynamics (MD) simulation plays an important role in understanding the catalytic mechanisms and is critical to the design of efficient and stable catalysts. Classical MD simulation with empirical potentials has a high computational efficiency but a limited accuracy, particularly for systems involving chemical reactions, and the accurate first-principle methods suffer from heavy computational costs and become unaffordable in most cases. The existing emerging machine-learning force field (MLFF) method is proven with affordable computational cost and first-principle-level accuracy. MLFF-assisted MD simulation can offer an effective approach for dynamics simulation in nanoscale catalysis. This review represented the fundamental principle of two main MLFF methods, i.e., the Behler-Parrinello atom-centered neural network method and the embedded-network-based deep potential. The applications of MLFF-assisted dynamic studies related to nano-scale catalysis (i.e., structure reconstruction and reaction processes in catalysis) were described. In addition, some possible future challenges of MLFF methods in dynamics simulation were also given.

, authors=Bo LIN, Shuangzhe ZHANG, Bai LI, Chuan ZHOU, Lei LI, authorsList=Bo LIN, Shuangzhe ZHANG, Bai LI, Chuan ZHOU, Lei LI, authorCompany=null, correspAuthors=Lei LI, 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=1319222578548429570, articleId=1319222576644215552, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=机器学习辅助的纳米催化反应动力学研究进展, 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=

动力学模拟是催化反应动力学研究的重要手段之一，有助于理解催化反应的内在机理，对于设计高效稳定的纳米催化剂十分重要。基于经验力场的经典分子动力学计算速度快，但计算精度有限。基于第一性原理的分子动力学方法精度高，但计算速度慢，难以大规模实施。近年来，机器学习力场（MLFF）方法被广泛应用于势能面的开发，基于MLFF的分子动力学（MLFF MD）方法兼顾计算速度与准确性，为催化反应动力学研究带来了新契机。本文首先回顾了MLFF势能面构造的主要方法，对基于对称函数的描述符设计原理和以嵌入式网络为基础的描述符构建方法进行了阐述，展示了MLFF MD方法应用于催化剂结构/组分演变和催化反应过程动力学模拟中的最新进展，进一步展望了MLFF在长时动力学模拟中所面临的挑战。

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LIN Bo (1991-), male, Ph.D. E-mail: linb@sustech.edu.cn

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LIN Bo (1991-), male, Ph.D. E-mail: linb@sustech.edu.cn

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林博（1991—），男，博士。

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林博（1991—），男，博士。

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