Machine learning for fast development of advanced energy materials

Bita Farhadi; Jiaxue You; Dexu Zheng; Lu Liu; Sajian Wu; Jianxun Li; Zhipeng Li; Kai Wang; Shengzhong Liu

Next Materials (Sep 2023)

Machine learning for fast development of advanced energy materials

Bita Farhadi,
Jiaxue You,
Dexu Zheng,
Lu Liu,
Sajian Wu,
Jianxun Li,
Zhipeng Li,
Kai Wang,
Shengzhong Liu

Affiliations

Bita Farhadi: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China
Jiaxue You: Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Shaanxi Engineering Lab for Advanced Energy Technology, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, Shaanxi, China
Dexu Zheng: China National Nuclear Power Co., Ltd. Beijing 100089, China
Lu Liu: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100039, China
Sajian Wu: China National Nuclear Power Co., Ltd. Beijing 100089, China
Jianxun Li: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100039, China
Zhipeng Li: China National Nuclear Power Co., Ltd. Beijing 100089, China
Kai Wang: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100039, China; Corresponding authors at: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China.
Shengzhong Liu: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100039, China; Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Shaanxi Engineering Lab for Advanced Energy Technology, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, Shaanxi, China; Corresponding authors at: Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China.

Journal volume & issue: Vol. 1, no. 3
p. 100025

Abstract

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With its unique advantages in artificial intelligence, data analysis, interpolation and numerical extrapolation, etc. ML has recently been quickly developed for the discovery of advanced energy materials. In particular, many algorithms have been developed to predict material properties. Herein, we first introduce the ML algorithms used in material science and the structure of each algorithm. Then we examine the algorithms that have been used recently in functional materials, especially in solar cells, batteries, and phase-change materials. Finally, advantages and disadvantages of each algorithm are compared to aid readers in choosing a suitable algorithm for specific applications.

Published in Next Materials

ISSN: 2949-8228 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology
Website: https://www.sciencedirect.com/journal/next-materials

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Abstract

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