Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation

doi:10.1016/j.jmps.2021.104481

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation
	Wen JC(温济慈); Zou, Qingrong 3; Wei YJ(魏宇杰)
刊名	JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
	2021-08
卷号	153 页码:104481
关键词	Physics-driven machine learning Lithium-metal anode Creep Finite-element analysis Constitutive model
ISSN号	0022-5096
DOI	10.1016/j.jmps.2021.104481
英文摘要	Precise understanding on the temperature and time-dependent deformation in lithium-metal anode is of compelling need for durable service of Li-based batteries. Due to both temporal and spatial intertwined thermal agitations and the scarcity of experiments, faithful deformation map of Li-metal covering a broad range of service condition is still lacking. Here we design a physicsdriven machine learning (PD-ML) algorithm to map the temperature, stress and rate-dependent deformation in Li-metal. We demonstrate that the PD-ML model, fed with limited experimental results, can predict the mechanical response of Li-metal in a wide span of temperature and deformation rate, and help to realize a deformation map of Li-metal with high fidelity. A finite element (FE) procedure based on the PD-ML constitutive model is then developed. The integration of PD-ML with FE procedure inherits the power of FE analysis and the accuracy originated from PD-ML in describing temperature, stress and rate-dependent mechanical response of Limetal. The method introduced here paves a new way for constitutive modelling to capture the complex deformation in solids involving multi-field and multiscale mechanics.
学科主题	Materials Science, Multidisciplinary ; Mechanics ; Physics, Condensed Matter
分类号	一类/力学重要期刊
语种	英语
WOS记录号	WOS:000663803400004
资助机构	NSFC Basic Science Center for 'Multiscale Problems in Nonlinear Mechanics' [11988102] ; National Natural Science Foundation of China (NSFC) [12002343] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB22020200] ; CAS Center for Excellence in Complex System Mechanics ; Scientific Research Foundation Project of Beijing Information Science and Technology University [2025032]
其他责任者	Wei, YJ (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China. ; Wei, YJ (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China.
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/90258]
专题	力学研究所_非线性力学国家重点实验室
作者单位	1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Beijing Informat Sci & Technol Univ, Sch Appl Sci, Beijing 100192, Peoples R China 3.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wen JC,Zou, Qingrong,Wei YJ. Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation[J]. JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS,2021,153:104481.
APA	温济慈,Zou, Qingrong,&魏宇杰.(2021).Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation.JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS,153,104481.
MLA	温济慈,et al."Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation".JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS 153(2021):104481.