Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation
文献类型：期刊论文
| 作者 | Yu, Tong1; Wang, Chunyang3; Yang, Huicong1,2; Li, Feng1,2 |
| 刊名 | JOURNAL OF ENERGY CHEMISTRY
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 90页码:191-204 |
| 关键词 | Metal-ion battery Machine learning Electrode materials Characterization Status evaluation |
| ISSN号 | 2095-4956 |
| DOI | 10.1016/j.jechem.2023.10.049 |
| 通讯作者 | Li, Feng(fli@imr.ac.cn) |
| 英文摘要 | 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. |
| WOS研究方向 | Chemistry ; Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001160050600001 |
| 出版者 | ELSEVIER |
| 源URL |  |
| 专题 | 金属研究所_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. |
入库方式： OAI收割
来源：金属研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。