Article(id=1319222587369050960, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220824, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664553600000, receivedDateStr=2022-10-01, revisedDate=1665590400000, revisedDateStr=2022-10-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456774799, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456774799, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456774799, creator=13701087609, updateTime=1792456774799, 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=476, endPage=487, ext={EN=ArticleExt(id=1319222587557794641, articleId=1319222587369050960, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Development and Application of Atomic Simulation Software Based on Machine Learning Potentials, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

Recent development of large-scale atomic simulation techniques based on machine learning has brought a great promise in chemistry. These simulations are featured by both high speed and high accuracy. This review outlined recent development on three key aspects of atomic simulation based on machine learning potential, i.e., machine learning models and structure descriptors, generation of global potential energy surface training sets, and automatic training of potential functions based on active learning. It is indicated that the designed structure descriptor and feedforward neural network model are suitable for generating a highly complex global potential energy surface. In addition, the applications of LASP software in material and reaction simulations were also selected to illustrate how ML-based atomic simulation could assist the discovery of novel materials and reactions.

, authors=Cheng SHANG, Peilin KANG, Zhipan LIU, authorsList=Cheng SHANG, Peilin KANG, Zhipan LIU, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, 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=1319222587847201618, articleId=1319222587369050960, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于机器学习势函数的原子模拟软件的开发及应用, 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=

近年来，基于机器学习的大规模原子模拟技术的发展为许多化学分支的发展带来了巨大的希望。这些模拟具有高速度和高精度的特点。本文概述了基于机器学习势函数的原子模拟的3个关键方面的最新进展，即机器学习模型和结构描述子的开发，全局势能面训练集的生成，以及基于主动学习的势函数自动训练。研究表明，最近设计的指数型结构描述子和前馈神经网络模型非常适合生成高度复杂的全局势能面。通过神经网络势函数在材料和反应模拟中的2个最新应用来说明基于机器学习势函数的原子模拟如何有助于发现新材料和反应。

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SHANG Cheng (1985-), male, Ph.D., Professor. E-mail: cshang@fudan.edu.cn

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SHANG Cheng (1985-), male, Ph.D., Professor. E-mail: cshang@fudan.edu.cn

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商城（1985—），男，博士，教授。

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商城（1985—），男，博士，教授。

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