Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation

doi:10.1016/j.jechem.2023.10.049

IMR OpenIR

	Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation
	Yu, Tong 1; Wang, Chunyang 3; Yang, Huicong 1,2; Li, Feng 1,2
通讯作者	Li, Feng(fli@imr.ac.cn)
	2024-03-01
发表期刊	JOURNAL OF ENERGY CHEMISTRY
ISSN	2095-4956
卷号	90 页码:191-204
摘要	Metal-ion batteries (MIBs), including alkali metal-ion (Li+, Na+, and K+), multi-valent metal-ion (Zn2+, Mg2+, and Al3+), metal-air, and metal-sulfur batteries, play an indispensable role in electrochemical energy storage. However, the performance of MIBs is significantly influenced by numerous variables, resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement. Machine learning (ML), with its capability to solve intricate tasks and perform robust data processing, is now catalyzing a revolutionary transformation in the development of MIB materials and devices. In this review, we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years. We present an extensive overview of existing algorithms, elucidating their details, advantages, and limitations in various applications, which encompass electrode screening, material property prediction, electrolyte formulation design, electrode material characterization, manufacturing parameter optimization, and real-time battery status monitoring. Finally, we propose potential solutions and future directions for the application of ML in advancing MIB development.
关键词	Metal-ion battery Machine learning Electrode materials Characterization Status evaluation
DOI	10.1016/j.jechem.2023.10.049
收录类别	SCI
语种	英语
WOS研究方向	Chemistry ; Energy & Fuels ; Engineering
WOS类目	Chemistry, Applied ; Chemistry, Physical ; Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:001160050600001
出版者	ELSEVIER
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/184566
专题	中国科学院金属研究所
通讯作者	Li, Feng
作者单位	1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Liaoning, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Liaoning, Peoples R China 3.Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA
推荐引用方式 GB/T 7714	Yu, Tong,Wang, Chunyang,Yang, Huicong,et al. Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation[J]. JOURNAL OF ENERGY CHEMISTRY,2024,90:191-204.
APA	Yu, Tong,Wang, Chunyang,Yang, Huicong,&Li, Feng.(2024).Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation.JOURNAL OF ENERGY CHEMISTRY,90,191-204.
MLA	Yu, Tong,et al."Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation".JOURNAL OF ENERGY CHEMISTRY 90(2024):191-204.