期刊
ADVANCED MATERIALS
卷 34, 期 25, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202101474
关键词
lithium-ion batteries; machine learning; materials discovery and prediction; state prediction
类别
资金
- Singapore MOE AcRF [2020-T1-001-031, 2017-T2-2-069]
- Nation Research Foundation, Prime Minister's Office, Singapore [NRF2017EWT-EP003-023, NRF2015ENC-GDCR01001-003]
- National Research foundation of Singapore [NRFI2017-08]
- AStar AME programmatic fund [A20H3g2140]
- Academic Research Fund Tier 1 [RG8/20, RG104/18]
- 111 project from Zhengzhou University [D18023]
AI technology and computational chemistry can accelerate the research and development of novel battery systems, with successful examples, challenges of deploying AI in real-world scenarios, and an integrated framework outlined. The applications of machine learning in property prediction and battery discovery, as well as the prediction of battery states, are further summarized.
Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional trial-and-error processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.
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