Machine learning in materials science

InfoMat

Xiang‐Yu Sun¹  Kun Xu¹  Jing Wei¹  Xuan Chu¹  Ming Lei¹  Zhongming Wei²  Hui‐Xiong Deng²  Jigen Chen³ 

[1] State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China;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;Zhejiang Provincial Key Laboratory for Cutting Tools Taizhou University Taizhou China;

关键词: data processing;    deep learning;    machine learning;    modeling;    validation;   

DOI  :  10.1002/inf2.12028

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown