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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202101474
|2 doi
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|a pubmed24n1101.xml
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|a eng
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| 100 |
1 |
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|a Lv, Chade
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Machine Learning
|b An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries
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|c 2022
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 23.06.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 Wiley-VCH GmbH.
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|a 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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|a Journal Article
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|a Review
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4 |
|a lithium-ion batteries
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a materials discovery and prediction
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| 650 |
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4 |
|a state prediction
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| 700 |
1 |
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|a Zhou, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhong, Lixiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Chunshuang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Srinivasan, Madhavi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Seh, Zhi Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Chuntai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pan, Hongge
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Shuzhou
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wen, Yonggang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Qingyu
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 25 vom: 25. Juni, Seite e2101474
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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|g volume:34
|g year:2022
|g number:25
|g day:25
|g month:06
|g pages:e2101474
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|u http://dx.doi.org/10.1002/adma.202101474
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