Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning

Abstract

Searching for novel, high-performance, two-dimensional photovoltaic (2DPV) materials is an important pursuit for solar cell applications. In this work, an efficient method based on the machine learning algorithm combined with high-throughput screening is developed. Twenty-six 2DPV candidates are successfully ruled out from 187093 experimentally identified inorganic crystal structures, whose conversion efficiencies are predicted by density functional theory calculations. Our results indicate that Sb2Se2Te, Sb2Te3, and Bi2Se3 exhibit conversion efficiencies that are much higher than those of others, which make them promising 2DPV candidates for further applications. The superior photovoltaic performance is then analyzed, and the hidden structure-related relationships with photovoltaic properties are established, thus providing important information for the further examination of 2DPV materials. Given the rapid development of the database of materials, this approach not only provides an efficient way of searching for novel 2DPV materials but also can be applied to exploration of a broad range of functional materials.