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Machine Learning in Lithium Battery Solid-State Electrolytes
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Xiang CHEN, Zhong-Heng FU, Yu-Chen GAO, Qiang ZHANG
Journal of the Chinese Ceramic Society | 2023, 51(2) : 488 - 498
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Journal of the Chinese Ceramic Society | 2023, 51(2): 488-498
• Review •
Machine Learning in Lithium Battery Solid-State Electrolytes
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Xiang CHEN, Zhong-Heng FU, Yu-Chen GAO, Qiang ZHANG
Affiliations
  • Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

    • CHEN Xiang (1994-), male, Ph.D., Assistant Professor. E-mail:

About Author:

CHEN Xiang (1994-), male, Ph.D., Assistant Professor. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220818
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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.

lithium batteries  /  solid-state electrolytes  /  machine learning  /  ionic conductivity  /  molecular dynamics simulations
陈翔, 富忠恒, 高宇辰, 张强. 机器学习在锂电池固态电解质研究中的应用. 硅酸盐学报, 2023 , 51 (2) : 488 -498 . DOI: 10.14062/j.issn.0454-5648.20220818
Xiang CHEN, Zhong-Heng FU, Yu-Chen GAO, Qiang ZHANG. Machine Learning in Lithium Battery Solid-State Electrolytes[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 488 -498 . DOI: 10.14062/j.issn.0454-5648.20220818
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Year 2023 volume 51 Issue 2
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doi: 10.14062/j.issn.0454-5648.20220818
  • Receive Date：2022-09-30
  • Online Date：2026-10-20
  • Published：2023-02-20
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  • Received：2022-09-30
  • Revised：2022-10-28
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    Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
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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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