Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154706
Title: Machine learning : an advanced platform for materials development and state prediction in lithium-ion batteries
Authors: Lv, Chade
Zhou, Xin
Zhong, Lixiang
Yan, Chunshuang
Srinivasan, Madhavi
Seh, Zhi Wei
Liu, Chuntai
Pan, Hongge
Li, Shuzhou
Wen, Yonggang
Yan, Qingyu
Keywords: Engineering::Materials::Energy materials
Issue Date: 2021
Source: 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-. https://dx.doi.org/10.1002/adma.202101474
Project: 2020-T1-001-031 
2017-T2-2-069 
NRF2017EWT-EP003-023 
NRF2015ENC-GDCR01001-003 
NRFI2017-08 
A20H3g2140 
RG8/20 
RG104/18 
Journal: Advanced Materials 
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.
URI: https://hdl.handle.net/10356/154706
ISSN: 1521-4095
DOI: 10.1002/adma.202101474
Rights: 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-, which has been published in final form at https://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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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