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Article(id=1319222543156883983, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220826, 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=1667491200000, revisedDateStr=2022-11-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456764258, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456764258, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456764258, creator=13701087609, updateTime=1792456764258, 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=531, endPage=543, ext={EN=ArticleExt(id=1319222543387570704, articleId=1319222543156883983, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

With the development of artificial intelligence technology, machine learning atomic interaction potential has become popular to solve a problem regarding the low accuracy of empirical potential. Machine learning atomic interaction potential avoids a low efficiency of conventional fitting method for empirical potential and becomes an emerging tool for material exploration and research. This review represented the characteristics of existing machine learning potential and the applications in phase change, intrinsic properties and interface researches. In addition, the challenge and development trends of machine learning atomic interaction potential were also prospected.

, authors=Jing WU1, 2, An HUANG1, Hanpeng XIE1, Donghai WEI1, Aonan LI1, Bo PENG1, Huimin WANG3, Zhenzhen QIN4, Te-huan LIU2, Guangzhao QIN1, authorsList=Jing WU, An HUANG, Hanpeng XIE, Donghai WEI, Aonan LI, Bo PENG, Huimin WANG, Zhenzhen QIN, Te-huan LIU, Guangzhao QIN, authorCompany=null, correspAuthors=Te-huan LIU, Guangzhao QIN, authorNote=

WU Jing (1998-), male, Doctoral candidate. E-mail:

, correspAuthorsNote=
LIU Te-huan (1984-), male, Ph.D., Professor. E-mail:
QIN Guangzhao (1991-), male, Ph.D., Professor.
, 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=1319222543630840337, articleId=1319222543156883983, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于机器学习势函数的材料力热性质多尺度模拟研究进展, 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=

随着人工智能技术的发展，采用机器学习方法进行势函数的构建和拟合，成为目前解决经验势函数精度问题的主要技术途径。机器学习方法解决了传统势函数拟合中的试错低效问题，已成为材料设计和物性研究不可或缺的有力工具。本文围绕当前机器学习势函数的特点，及其在相变研究、本征性质研究和界面研究等方面的应用，全面总结介绍势函数及其拟合策略，以及其在特定物性研究中的应用，推动机器学习势函数在材料力热性质的多尺度模拟研究。最后，展望了机器学习势函数所面临的挑战和未来发展前景。

, authors=吴静1, 2, 黄安1, 谢涵鹏1, 魏东海1, 李奥南1, 彭博1, 王慧敏3, 秦真真4, 刘德欢2, 秦光照1, authorsList=吴静, 黄安, 谢涵鹏, 魏东海, 李奥南, 彭博, 王慧敏, 秦真真, 刘德欢, 秦光照, authorCompany=null, correspAuthors=刘德欢, 秦光照, authorNote=

吴静（1998—），男，博士研究生。

, correspAuthorsNote=
刘德欢（1984—），男，博士，教授；
秦光照（1991—），男，博士，教授。
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WU Jing (1998-), male, Doctoral candidate. E-mail:

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WU Jing (1998-), male, Doctoral candidate. E-mail:

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吴静（1998—），男，博士研究生。

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基于机器学习势函数的材料力热性质多尺度模拟研究进展
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吴静 1, 2 , 黄安 1 , 谢涵鹏 1 , 魏东海 1 , 李奥南 1 , 彭博 1 , 王慧敏 3 , 秦真真 4 , 刘德欢 2 , 秦光照 1
硅酸盐学报 | 机器学习在材料研发中的应用专题—综合评述 2023,51(2): 531-543
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硅酸盐学报 |机器学习在材料研发中的应用专题—综合评述 2023 , 51 (2) : 531 -543
基于机器学习势函数的材料力热性质多尺度模拟研究进展
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吴静1, 2 , 黄安1, 谢涵鹏1, 魏东海1, 李奥南1, 彭博1, 王慧敏3, 秦真真4, 刘德欢2 , 秦光照1
作者信息
  • 1．湖南大学机械与运载工程学院，汽车车身先进设计制造国家重点实验室，长沙 410082
  • 2．华中科技大学能源与动力工程学院，武汉 430074
  • 3．湘潭大学物理与光电工程学院，湖南 湘潭 411105
  • 4．郑州大学物理学院（微电子学院），郑州 450001
通讯作者:
刘德欢（1984—），男，博士，教授；
秦光照（1991—），男，博士，教授。
作者简介:

吴静（1998—），男，博士研究生。

Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential
Jing WU1, 2 , An HUANG1, Hanpeng XIE1, Donghai WEI1, Aonan LI1, Bo PENG1, Huimin WANG3, Zhenzhen QIN4, Te-huan LIU2 , Guangzhao QIN1
Affiliations
  • 1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
  • 2.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 3.Hunan Key Laboratory for Micro-Nano Energy Materials & Device and School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, Hunan, China
  • 4.School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
    • Corresponding Author:
    LIU Te-huan (1984-), male, Ph.D., Professor. E-mail:
    QIN Guangzhao (1991-), male, Ph.D., Professor.

    WU Jing (1998-), male, Doctoral candidate. E-mail:

出版时间: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220826
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随着人工智能技术的发展，采用机器学习方法进行势函数的构建和拟合，成为目前解决经验势函数精度问题的主要技术途径。机器学习方法解决了传统势函数拟合中的试错低效问题，已成为材料设计和物性研究不可或缺的有力工具。本文围绕当前机器学习势函数的特点，及其在相变研究、本征性质研究和界面研究等方面的应用，全面总结介绍势函数及其拟合策略，以及其在特定物性研究中的应用，推动机器学习势函数在材料力热性质的多尺度模拟研究。最后，展望了机器学习势函数所面临的挑战和未来发展前景。

机器学习  /  原子相互作用势  /  多尺度  /  力热性质

With the development of artificial intelligence technology, machine learning atomic interaction potential has become popular to solve a problem regarding the low accuracy of empirical potential. Machine learning atomic interaction potential avoids a low efficiency of conventional fitting method for empirical potential and becomes an emerging tool for material exploration and research. This review represented the characteristics of existing machine learning potential and the applications in phase change, intrinsic properties and interface researches. In addition, the challenge and development trends of machine learning atomic interaction potential were also prospected.

machine learning  /  atomic interaction potential  /  multiscale  /  mechanical and thermal properties
吴静, 黄安, 谢涵鹏, 魏东海, 李奥南, 彭博, 王慧敏, 秦真真, 刘德欢, 秦光照. 基于机器学习势函数的材料力热性质多尺度模拟研究进展. 硅酸盐学报, 2023 , 51 (2) : 531 -543 . DOI: 10.14062/j.issn.0454-5648.20220826
Jing WU, An HUANG, Hanpeng XIE, Donghai WEI, Aonan LI, Bo PENG, Huimin WANG, Zhenzhen QIN, Te-huan LIU, Guangzhao QIN. Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 531 -543 . DOI: 10.14062/j.issn.0454-5648.20220826
基金
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  • 国家自然科学基金(52006057)
  • 湖南大学汽车车身先进设计制造国家重点实验室自主研究课题(52175013)
  • 国家自然科学基金(52076089)
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doi: 10.14062/j.issn.0454-5648.20220826
  • 接收时间：2022-10-01
  • 首发时间：2026-10-20
  • 出版时间：2023-02-20
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  • 收稿日期：2022-10-01
  • 修回日期：2022-11-04
基金
国家自然科学基金(52006057)
湖南大学汽车车身先进设计制造国家重点实验室自主研究课题(52175013)
国家自然科学基金(52076089)
作者信息
    1．湖南大学机械与运载工程学院，汽车车身先进设计制造国家重点实验室，长沙 410082
    2．华中科技大学能源与动力工程学院，武汉 430074
    3．湘潭大学物理与光电工程学院，湖南 湘潭 411105
    4．郑州大学物理学院（微电子学院），郑州 450001

通讯作者:

刘德欢（1984—），男，博士，教授；
秦光照（1991—），男，博士，教授。
参考文献
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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20220826
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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