Machine learning in energy storage materials. Issue 2 (29th March 2022)

• Record Type: Journal Article
• Authors: Shen, Zhong‐Hui; Liu, Han‐Xing; Shen, Yang; Hu, Jia‐Mian; Chen, Long‐Qing; Nan, Ce‐Wen
• Is Part Of: Interdisciplinary materials. Volume 1:Issue 2(2022)
• Page Start: 175
• Page End: 195
• Publication Date: 2022-03-29
• Subjects: dielectric capacitor -- energy storage -- lithium‐ion battery -- machine learning; Materials science; Science; Periodicals; 620.11
• DOI: 10.1002/idm2.12020
• Languages: English
• ISSNs: 2767-4401

Abstract

Abstract: With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium‐ion batteries as two representative examples, we review substantial advances of machine learning in the research and development of energy storage materials. First, a thorough discussion of the machine learning framework in materials science is presented. Then, we summarize the applications of machine learning from three aspects, including discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization. Finally, a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science. Abstract : Machine learning is transforming the research paradigm of materials science in recent years. This review summarizes the recent advances of machine learning in the research and development of energy storage materials and provides some insights on the innovative implementation of machine learning in materials science.