Materials discovery and design using machine learning  

作　　者：Yue Liu Tianlu Zhao Wangwei Ju Siqi Shi 

机构地区：[1]School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China [2]School of Materials Science and Engineering,Shanghai University,Shanghai 200444,China [3]Materials Genome Institute,Shanghai University,Shanghai 200444,China

出　　处：《Journal of Materiomics》2017年第3期159-177,共19页无机材料学学报（英文）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant Nos.U1630134,51622207 and 51372228);the National Key Research and Development Program of China(Grant Nos.2017YFB0701600 and 2017YFB0701500);the Shanghai Institute of Materials Genome from the Shanghai Municipal Science and Technology Commission(Grant No.14DZ2261200);the Shanghai Municipal Education Commission(Grant No.14ZZ099);the Natural Science Foundation of Shanghai(Grant No.16ZR1411200).

摘　　要：The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships(QSARs),among other issues,are hot topics in the field of materials science.Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations.Thus,it is imperative to develop a new method of accelerating the discovery and design process for novel materials.Recently,materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.In this review,we first outline the typical mode of and basic procedures for applying machine learning in materials science,and we classify and compare the main algorithms.Then,the current research status is reviewed with regard to applications of machine learning in material property prediction,in new materials discovery and for other purposes.Finally,we discuss problems related to machine learning in materials science,propose possible solutions,and forecast potential directions of future research.By directly combining computational studies with experiments,we hope to provide insight into the parameters that affect the properties of materials,thereby enabling more efficient and target-oriented research on materials discovery and design.

关 键 词：New materials discovery Materials design Materials properties prediction Machine learning 

分 类 号：H31[语言文字—英语]