Machine learning for fast development of advanced energy materials

With its unique advantages in artificial intelligence, data analysis, interpolation and numerical extrapolation, etc. ML has recently been quickly developed for the discovery of advanced energy materials. In particular, many algorithms have been developed to predict material properties. Herein, we first introduce the ML algorithms used in material science and the structure of each algorithm. Then we examine the algorithms that have been used recently in functional materials, especially in solar cells, batteries, and phase-change materials. Finally, advantages and disadvantages of each algorithm are compared to aid readers in choosing a suitable algorithm for specific applications.