Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique
Qionghua Zhou, Shuaihua Lu, Yilei Wu, Jinlan Wang
Abstract
Property-oriented material design is a persistent pursuit for material scientists. Recently, machine learning (ML) as a powerful new tool has attracted worldwide attention in the material design field. Based on statistics instead of solving physical equations, ML can predict material properties faster with lower cost. Because of its data-driven characteristics, the quantity and quality of material data become the keys to the practical applications of this technique. In this Perspective, problems caused by lack of data and diversity of data are discussed. Various approaches, including high-throughput calculations, database construction, feedback loop algorithms, and better descriptors, have been exploited to address these problems. It is expected that this Perspective will bring data itself to the forefront of ML-based material design.