A Survey of Artificial Intelligence Techniques Applied in Energy Storage Materials R&D

Ziyi Luo; Xinyi Yang; Yingxue Wang; Weidi Liu; Siliang Liu; Yuankun Zhu; Zihan Huang; Hong Zhang; Shuming Dou; Jie Xu; Jiachen Tian; Kui Xu; Xiaowang Zhang; Wenbin Hu; Yida Deng

doi:10.3389/fenrg.2020.00116

Frontiers in Energy Research (Jul 2020)

A Survey of Artificial Intelligence Techniques Applied in Energy Storage Materials R&D

Ziyi Luo,
Xinyi Yang,
Yingxue Wang,
Weidi Liu,
Siliang Liu,
Yuankun Zhu,
Zihan Huang,
Hong Zhang,
Shuming Dou,
Jie Xu,
Jiachen Tian,
Kui Xu,
Xiaowang Zhang,
Wenbin Hu,
Yida Deng

Affiliations

Ziyi Luo: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Xinyi Yang: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Yingxue Wang: National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data, Beijing, China
Weidi Liu: Materials Engineering, The University of Queensland, Brisbane, QLD, Australia
Siliang Liu: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Yuankun Zhu: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Zihan Huang: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Hong Zhang: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Shuming Dou: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Jie Xu: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Jiachen Tian: College of Intelligence and Computing, Tianjin University, Tianjin, China
Kui Xu: School of Life Sciences, Tsinghua University, Beijing, China
Xiaowang Zhang: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Wenbin Hu: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China
Yida Deng: Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education, Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, China

DOI: https://doi.org/10.3389/fenrg.2020.00116
Journal volume & issue: Vol. 8

Abstract

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Energy shortage is a severe challenge nowadays. It has affected the development of new energy sources. Artificial intelligence (AI), such as learning and analyzing, has been widely used for various advantages. It has been successfully applied to predict materials, especially energy storage materials. In this paper, we present a survey of the present status of AI in energy storage materials via capacitors and Li-ion batteries. We picture the comprehensive progress of AI in energy storage materials, including the advantages and disadvantages of material data to support AI. Finally, we provide some ideas to solve those challenges.

Published in Frontiers in Energy Research

ISSN: 2296-598X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: General Works
Website: https://www.frontiersin.org/journals/energy-research

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