Machine Learning in Lithium Battery Solid-State Electrolytes

被引：0
|
作者：
Chen X. [1 ]
Fu Z.-H. [1 ]
Gao Y.-C. [1 ]
Zhang Q. [1 ]
机构：
[1] Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing
来源：
Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society | 2023年 / 51卷 / 02期
关键词：
ionic conductivity; lithium batteries; machine learning; molecular dynamics simulations; solid-state electrolytes;
D O I：
10.14062/j.issn.0454-5648.20220818
中图分类号：
学科分类号：
摘要：
Solid-sate lithium battery (SSB) is considered as one of the most promising next-generation batteries due to its high energy density. The emergence of machine-learning (ML) techniques affords a possibility for the study of solid-state electrolytes (SSEs). ML is able to promote a deep application of theoretical simulations in SSB and build a high-accuracy and multi-scale simulation paradigm. Besides, ML can establish a quantitative structure–function relation of SSEs and achieve a high-throughput screening of advanced SSEs. In addition, ML-assisted experiments can synthesize advanced SSEs with a high efficiency and deliver a comprehensive understanding of working mechanism in SSBs with various characterizations such as synchrotron imaging. Therefore, the introduction of ML and its combination with the existing theoretical and experimental methods can promote the study of SSEs and the practical application of SSBs definitely. © 2023 Chinese Ceramic Society. All rights reserved.
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页码：488 / 498
页数：10
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