Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery

Chen Yawei; Liu Yue; He Zixu; Xu Liang; Yu Peiping; Sun Qintao; Li Wanxia; Jie Yulin; Cao Ruiguo; Cheng Tao; Jiao Shuhong

doi:10.1360/nso/20230039

National Science Open (Dec 2023)

Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery

Chen Yawei,
Liu Yue,
He Zixu,
Xu Liang,
Yu Peiping,
Sun Qintao,
Li Wanxia,
Jie Yulin,
Cao Ruiguo,
Cheng Tao,
Jiao Shuhong

Affiliations

Chen Yawei: ["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]
Liu Yue: ["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]
He Zixu: ["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]
Xu Liang: ["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]
Yu Peiping: ["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]
Sun Qintao: ["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]
Li Wanxia: ["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]
Jie Yulin: ["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]
Cao Ruiguo: ["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]
Cheng Tao: ["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China","Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou 215123, China"]
Jiao Shuhong: ["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]

DOI: https://doi.org/10.1360/nso/20230039
Journal volume & issue: Vol. 3

Abstract

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Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries, along with the urgent need for more sophisticated methods of analysis, this comprehensive review underscores the promise of machine learning (ML) models in this research field. It explores the application of these innovative methods to studying battery interfaces, particularly focusing on lithium metal anodes. Amid the limitations of traditional experimental techniques, the review supports a hybrid approach that couples experimental and simulation methods, enabling granular insights into the formation process and characteristics of battery interfaces at the molecular level and harnessing AI to extract patterns from voluminous data sets. It showcases the utility of such techniques in electrolyte design and battery life prediction and introduces a novel perspective on battery interface mechanisms. The review concludes by asserting the potential of artificial intelligence (AI) or ML models as invaluable tools in the future of battery research and highlights the importance of fostering confidence in these technologies within the scientific community.

Published in National Science Open

ISSN: 2097-1168 (Print); 2097-1400 (Online)
Publisher: Science Press
Country of publisher: China
LCC subjects: Science; Technology: Engineering (General). Civil engineering (General)
Website: https://www.sciengine.com/NSO/home

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