State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
Xuan Chu
State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
Xiang‐Yu Sun
State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
Kun Xu
State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
Hui‐Xiong Deng
State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China
Jigen Chen
Zhejiang Provincial Key Laboratory for Cutting Tools Taizhou University Taizhou China
Zhongming Wei
State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China
Ming Lei
State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.
