Article(id=1319222553332273717, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220639, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659542400000, receivedDateStr=2022-08-04, revisedDate=1659974400000, revisedDateStr=2022-08-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456766684, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456766684, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456766684, creator=13701087609, updateTime=1792456766684, 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=438, endPage=451, ext={EN=ArticleExt(id=1319222553554571830, articleId=1319222553332273717, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Macro-/Micro-Design of Electrochemical Energy Battery Based on Machine Learning, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

The energy storage systems are an important basis for electric vehicles and electronic devices. The existing battery design based on machine learning is able to quickly connect the complex relationship among material microstructure, material properties, and battery macroscopic properties. This review represented the applications and prospects of machine learning in micro-material design and state estimation of batteries. The data sources of machine learning battery design, advantages and disadvantages of algorithms and their application scenarios in the battery field, related innovative work in recent years and their prospects were discussed. This review can provide a reference for machine learning in the macro-/micro-design of energy storage systems.

, authors=Jinjin LI, Junfei CAI, Yanqiang HAN, Zhilong WANG, An CHEN, Simin YE, authorsList=Jinjin LI, Junfei CAI, Yanqiang HAN, Zhilong WANG, An CHEN, Simin YE, authorCompany=null, correspAuthors=null, authorNote=

LI Jinjin (1985-), female, Ph.D., Professor. E-mail:

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能源储存系统是电动汽车、电子设备等高新技术的重要基础。近年来基于机器学习的电池设计能够快速连结材料微观结构-材料微观性能-电池宏观性能的复杂关系,成为了热点研究。本文从能源电池的微观材料设计和宏观状态预测两方面系统性地综述了电池设计中机器学习的应用现状和前景,概括综述了机器学习电池设计的研究数据来源、算法的优缺点及其在电池领域的应用场景以及近年来的相关创新性工作及其展望,以期为机器学习在能源储存系统的宏微观设计提供了参考。

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李金金(1985—),女,博士,研究员。

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基于机器学习的电化学能源电池宏微观设计
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李金金 , 蔡俊飞 , 韩彦强 , 汪志龙 , 陈安 , 叶思敏
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): 438-451
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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) : 438 -451
基于机器学习的电化学能源电池宏微观设计
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李金金 , 蔡俊飞, 韩彦强, 汪志龙, 陈安, 叶思敏
作者信息
  • 上海交通大学电子信息与电气工程学院,薄膜与微细加工教育部重点实验室,上海 200240
作者简介:

李金金(1985—),女,博士,研究员。

Macro-/Micro-Design of Electrochemical Energy Battery Based on Machine Learning
Jinjin LI , Junfei CAI, Yanqiang HAN, Zhilong WANG, An CHEN, Simin YE
Affiliations
  • Shanghai Jiao Tong University and Electrical Engineering, Key Laboratory of Film and Microfabrication, Ministry of Education, Shanghai 200240, China
  • LI Jinjin (1985-), female, Ph.D., Professor. E-mail:

出版时间: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220639
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能源储存系统是电动汽车、电子设备等高新技术的重要基础。近年来基于机器学习的电池设计能够快速连结材料微观结构-材料微观性能-电池宏观性能的复杂关系,成为了热点研究。本文从能源电池的微观材料设计和宏观状态预测两方面系统性地综述了电池设计中机器学习的应用现状和前景,概括综述了机器学习电池设计的研究数据来源、算法的优缺点及其在电池领域的应用场景以及近年来的相关创新性工作及其展望,以期为机器学习在能源储存系统的宏微观设计提供了参考。

机器学习  /  数据挖掘  /  电池材料  /  电池状态

The energy storage systems are an important basis for electric vehicles and electronic devices. The existing battery design based on machine learning is able to quickly connect the complex relationship among material microstructure, material properties, and battery macroscopic properties. This review represented the applications and prospects of machine learning in micro-material design and state estimation of batteries. The data sources of machine learning battery design, advantages and disadvantages of algorithms and their application scenarios in the battery field, related innovative work in recent years and their prospects were discussed. This review can provide a reference for machine learning in the macro-/micro-design of energy storage systems.

machine learning  /  data mining  /  battery materials  /  battery state
李金金, 蔡俊飞, 韩彦强, 汪志龙, 陈安, 叶思敏. 基于机器学习的电化学能源电池宏微观设计. 硅酸盐学报, 2023 , 51 (2) : 438 -451 . DOI: 10.14062/j.issn.0454-5648.20220639
Jinjin LI, Junfei CAI, Yanqiang HAN, Zhilong WANG, An CHEN, Simin YE. Macro-/Micro-Design of Electrochemical Energy Battery Based on Machine Learning[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 438 -451 . DOI: 10.14062/j.issn.0454-5648.20220639
  • 国家科技部重点研发计划(2021YFC2100100)
  • 国家自然科学基金(21901157)
  • 上海市自然科学基金(21JC1403400)
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2023年第51卷第2期
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doi: 10.14062/j.issn.0454-5648.20220639
  • 接收时间:2022-08-04
  • 首发时间:2026-10-20
  • 出版时间:2023-02-20
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  • 收稿日期:2022-08-04
  • 修回日期:2022-08-09
基金
国家科技部重点研发计划(2021YFC2100100)
国家自然科学基金(21901157)
上海市自然科学基金(21JC1403400)
作者信息
    上海交通大学电子信息与电气工程学院,薄膜与微细加工教育部重点实验室,上海 200240
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2种不同金属材料的力学参数
科
Family
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Number of
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种数
Number of
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占总种数比例
Percentage of
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属
Genus
种数
Number of
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占总种数比例
Percentage of total
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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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