Article(id=1319222558516433508, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220793, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664121600000, receivedDateStr=2022-09-26, revisedDate=1666540800000, revisedDateStr=2022-10-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456767920, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456767920, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456767920, creator=13701087609, updateTime=1792456767920, 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=469, endPage=475, ext={EN=ArticleExt(id=1319222560303207013, articleId=1319222558516433508, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Analysis of Li Metal Anode by 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=

Li metal is regarded as an ideal anode for the next-generation secondary batteries. However, the growth of Li-dendrite results in a low coulomb efficiency, thus restricting the commercial application of Li secondary batteries. The mechanism of Li deposition and growth in an atomic scale is still unclear, and there are different opinions about the origin of Li dendrite. Recent work has dealt with the application of machine learning in computational materials science. This review represented the applications of machine learning potential for the study of Li metal anode.

, authors=Genming LAI1, Junyu JIAO1, Yao JIANG2, Jiaxin ZHENG1, 2, Chuying OUYANG2, authorsList=Genming LAI, Junyu JIAO, Yao JIANG, Jiaxin ZHENG, Chuying OUYANG, authorCompany=null, correspAuthors=Jiaxin ZHENG, authorNote=

LAI Genming (1998-), male, Doctoral candidate. E-mail:

, correspAuthorsNote=
ZHENG Jiaxin (1985-), male, Ph.D., Associate Professor. E-mail:
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锂金属是下一代二次电池的理想负极材料。然而,锂枝晶生长存在安全隐患,并导致电池Coulombic效率低,这严重制约了锂二次电池的商业应用。目前,人们对锂的沉积生长机制在原子尺度上还了解甚少,同时对锂枝晶的成因也众说纷纭。近年来,机器学习在计算材料学中的应用使得许多以前无法实现的进步成为可能,本文综述了机器学习原子势在锂金属负极研究中的应用。

, authors=赖根明1, 焦君宇1, 蒋耀2, 郑家新1, 2, 欧阳楚英2, authorsList=赖根明, 焦君宇, 蒋耀, 郑家新, 欧阳楚英, authorCompany=null, correspAuthors=郑家新, authorNote=

赖根明(1998—),男,博士研究生。

, correspAuthorsNote=
郑家新(1985—),男,博士,副教授。
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LAI Genming (1998-), male, Doctoral candidate. E-mail:

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赖根明(1998—),男,博士研究生。

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机器学习原子势在锂金属负极中的应用
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赖根明 1 , 焦君宇 1 , 蒋耀 2 , 郑家新 1, 2 , 欧阳楚英 2
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): 469-475
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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) : 469 -475
机器学习原子势在锂金属负极中的应用
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赖根明1 , 焦君宇1, 蒋耀2, 郑家新1, 2 , 欧阳楚英2
作者信息
  • 1.北京大学深圳研究生院新材料学院,广东 深圳 518055
  • 2.宁德时代21C创新实验室,福建 宁德 352100
通讯作者:
郑家新(1985—),男,博士,副教授。
作者简介:

赖根明(1998—),男,博士研究生。

Analysis of Li Metal Anode by Machine Learning Potential
Genming LAI1 , Junyu JIAO1, Yao JIANG2, Jiaxin ZHENG1, 2 , Chuying OUYANG2
Affiliations
  • 1.School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, Guangdong, China
  • 2.Fujian science & technology innovation laboratory for energy devices of China (21C-LAB), Ningde 352100, Fujian, China
  • Corresponding Author:
    ZHENG Jiaxin (1985-), male, Ph.D., Associate Professor. E-mail:

    LAI Genming (1998-), male, Doctoral candidate. E-mail:

出版时间: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220793
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锂金属是下一代二次电池的理想负极材料。然而,锂枝晶生长存在安全隐患,并导致电池Coulombic效率低,这严重制约了锂二次电池的商业应用。目前,人们对锂的沉积生长机制在原子尺度上还了解甚少,同时对锂枝晶的成因也众说纷纭。近年来,机器学习在计算材料学中的应用使得许多以前无法实现的进步成为可能,本文综述了机器学习原子势在锂金属负极研究中的应用。

锂金属负极  /  机器学习  /  分子动力学  /  计算模拟

Li metal is regarded as an ideal anode for the next-generation secondary batteries. However, the growth of Li-dendrite results in a low coulomb efficiency, thus restricting the commercial application of Li secondary batteries. The mechanism of Li deposition and growth in an atomic scale is still unclear, and there are different opinions about the origin of Li dendrite. Recent work has dealt with the application of machine learning in computational materials science. This review represented the applications of machine learning potential for the study of Li metal anode.

lithium metal anode  /  machine learning  /  molecular dynamics  /  simulation
赖根明, 焦君宇, 蒋耀, 郑家新, 欧阳楚英. 机器学习原子势在锂金属负极中的应用. 硅酸盐学报, 2023 , 51 (2) : 469 -475 . DOI: 10.14062/j.issn.0454-5648.20220793
Genming LAI, Junyu JIAO, Yao JIANG, Jiaxin ZHENG, Chuying OUYANG. Analysis of Li Metal Anode by Machine Learning Potential[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 469 -475 . DOI: 10.14062/j.issn.0454-5648.20220793
  • 国家自然科学基金(52272180; 12174162; 51962010)
  • 宁德时代创新实验室21C-LAB、IER基金2021(IERF202104)
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2023年第51卷第2期
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doi: 10.14062/j.issn.0454-5648.20220793
  • 接收时间:2022-09-26
  • 首发时间:2026-10-20
  • 出版时间:2023-02-20
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  • 收稿日期:2022-09-26
  • 修回日期:2022-10-24
基金
国家自然科学基金(52272180; 12174162; 51962010)
宁德时代创新实验室21C-LAB、IER基金2021(IERF202104)
作者信息
    1.北京大学深圳研究生院新材料学院,广东 深圳 518055
    2.宁德时代21C创新实验室,福建 宁德 352100

通讯作者:

郑家新(1985—),男,博士,副教授。
参考文献
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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20220793
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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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