Catalyze Materials Science with Machine Learning

Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal. Thanks to the nourishment of computer hardware and computational chemistry, the development of calculated scientific data repositories could fuel the ML models to investigate the vast materials space. At this moment, understanding this revolutionary paradigm is urgent, and this Review aims to deliver comprehensive information about the collaboration of ML with materials science. This Review summarizes recent achievements in catalysts design, which can be benefitted from ML because of the complex nature of catalytic reactions and vast candidate materials space. ML models for catalyst design could be transferred to applications in other domains and vice versa. The basic concepts of ML algorithms and practical guides to materials scientists are also demonstrated. Moreover, challenges and strategies of applying ML are discussed, which should be addressed collaboratively between materials scientists and ML communities. Ultimate integrations of ML in materials science are expected to accelerate the design, discovery, optimization, and interpretation of materials in both industry and academia, and this Review hopes to be the informative base camp for that journey.