Machine learning in materials science

被引：609

作者：

Wei, Jing ^{[1
]}

Chu, Xuan ^{[1
]}

Sun, Xiang-Yu ^{[1
]}

Xu, Kun ^{[1
]}

Deng, Hui-Xiong ^{[2
]}

Chen, Jigen ^{[3
]}

Wei, Zhongming ^{[2
,4
]}

Lei, Ming ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing, Peoples R China

[2] Univ Chinese Acad Sci, State Key Lab Superlattices & Microstruct, Ctr Mat Sci & Optoelect Engn, Inst Semicond,Chinese Acad Sci, Beijing 100083, Peoples R China

[3] Taizhou Univ, Zhejiang Prov Key Lab Cutting Tools, Taizhou, Peoples R China

[4] Beijing Acad Quantum Informat Sci, Beijing, Peoples R China

来源：

INFOMAT | 2019年 / 1卷 / 03期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

data processing; deep learning; machine learning; modeling; validation; SUPPORT VECTOR MACHINES; CONVOLUTIONAL NEURAL-NETWORKS; ORGANIC PHOTOVOLTAICS; REINFORCED-CONCRETE; CROSS-VALIDATION; FORCE-FIELD; DEEP; DESIGN; CLASSIFICATION; DISCOVERY;

D O I：

10.1002/inf2.12028

中图分类号：

T [工业技术];

学科分类号：

08 ;

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.

引用

页码：338 / 358

页数：21

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