Simulation and design of energy materials accelerated by machine learning

In the light of mature mathematical algorithms and material database construction, a basic research framework of machine learning (ML) method integrated with computational chemistry toolkits exhibits great potentials and advantages in the field of material researches. In this review, we introduce a work flow of ML in energy materials and demonstrate its recent applications in accelerating the material exploration, especially significant progresses in designing novel catalysts, organic and inorganic battery materials and metal–organic framework materials. As a rising research direction, we also identify the prospects and challenges of ML. More automated and intelligent workflows will be widely used in energy material design with the development of ML. Our review provides a guideline to study and design energy materials in the framework of ML. This article is categorized under: Structure and Mechanism > Computational Materials Science