The typical mode of and basic procedures for applying machine learning in materials science are summarized and discussed. •For various points of application, the machine learning methods used for different purposes are comprehensively reviewed. •Existing problems are discussed, possible solutions are proposed and potential directions of future research are suggested. AbstractThe screening of novel materials with good performance and the modelling of quantitative structure-activity 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. Graphical abstractMachine learning provides a new means of screening novel materials with good performance, developing quantitative structure-activity relationships (QSARs) and other models, predicting the properties of materials, discovering new materials and performing other materials-relateds studies. 
KeywordsNew materials discovery Materials design Materials properties prediction Machine learning Recommended articles
Y. Liu obtained her B.S. and M.S. in computer science from Jiangxi Normal University in 1997 and 2000. She finished her Ph.D. in control theory and control engineering from Shanghai University (SHU) in 2005. She has been working with the School of Computer Engineering and Science of SHU since July 2000. During that time, she has been a curriculum R&D manager at the Sybase-SHU IT Institute of Sybase Inc. from July 2003 to July 2004 and a visiting scholar at the University of Melbourne from Sep. 2012 to Sep. 2013. Her current main research interests are focused on machine learning and its applications in materials science and demand forecasting. T. Zhao is a graduate candidate in the School of Computer Engineering and Science, Shanghai University, China. He received his Bachelor of Engineering degree in computer science from the School of Computer and Software, Nanjing University of Information Science & Technology, China, in 2015. His main research interests are focused on machine learning for predicting the properties of lithium-ion batteries. W. J. received his B.S. in computer science from Anhui Normal University in 2013. He finished his M.S. in computer science from Shanghai University in 2016. His main research interests are focused on machine learning for predicting the properties of lithium-ion batteries. S. S. obtained his B.S. from Jiangxi Normal University in 1998. He finished his Ph.D. from the Institute of Physics, Chinese Academy of Sciences, in 2004. After that, he joined the National Institute of Advanced Industrial Science and Technology of Japan and Brown University in the USA as a senior research associate, where he remained until joining Shanghai University as a professor in early 2013. His research interests are focused on the fundamentals and microscopic design of energy storage and conversion materials related to lithium-ion batteries and CeO2-based solid-state oxide fuel cells. |
