Machine Learning Accelerates the Discovery of Light-Absorbing Materials for Double Perovskite Solar Cells

Abstract

Double perovskite materials have excellent electronic and optical properties, which are the star material in the photovoltaic field. However, the large number of family members has brought difficulties to traditional material screening methods. The emergence of machine learning provides new ideas for this problem, and how to apply machine learning to search new functional materials has become a hot research topic. This paper combines machine learning and density functional theory (DFT) calculations to develop a goal-driven method to search for functional materials, aiming to select one that has suitable photoelectric properties and thermal stability from tens of thousands of double perovskite materials. Through a series of processes such as training excellent machine learning models, multicondition combination screening, and DFT calculations, 10 excellent double perovskite materials were finally selected from 16,400 candidate materials to guide subsequent experimental synthesis. Combining the high efficiency of machine learning and the accuracy of DFT calculations compensates for their respective shortcomings and improves the efficiency of finding new materials. In addition, this method also has a certain general applicability and can be extended to the screening of other materials.