Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

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Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
Title:
Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
Other Titles:
Advanced Materials
Publication Date:
07 September 2021
Citation:
Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., & Yan, Q. (2021). Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries. Advanced Materials, 2101474. Portico. https://doi.org/10.1002/adma.202101474
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Ministry of Education - Academic Research Fund Tier (AcRF)
Grant Reference no. : 2020-T1-001-031

This research / project is supported by the Ministry of Education - Academic Research Fund Tier (AcRF)
Grant Reference no. : 2017-T2-2-069

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 1
Grant Reference no. : (RG8/20)

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 1
Grant Reference no. : Tier 1 (RG104/18)

This research / project is supported by the National Research foundation - Investigatorship
Grant Reference no. : NRFI2017-08

This research / project is supported by the A*STAR - AME Programmatic
Grant Reference no. : A20H3g2140
Description:
This is the peer reviewed version of the following article: Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., & Yan, Q. (2021). Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries. Advanced Materials, 2101474. Portico. https://doi.org/10.1002/adma.202101474, which has been published in final form at doi.org/10.1002/adma.202101474. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited
ISSN:
0935-9648
1521-4095
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