Materials discovery and design using machine learning

The 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. Machine 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. • 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.