Machine learning in materials science

Jing Wei, Xuan Chu, Xiang Yu Sun, Kun Xu, Hui Xiong Deng, Jigen Chen, Zhongming Wei*, Ming Lei*

*此作品的通讯作者

科研成果: 期刊稿件文献综述同行评审

559 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)338-358
页数21
期刊InfoMat
1
3
DOI
出版状态已出版 - 9月 2019
已对外发布

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