Predicting Inorganic Photovoltaic Materials with Efficiencies >26% via Structure-Relevant Machine Learning and Density Functional Calculations

Abstract

Summary Discovering new inorganic photovoltaic materials becomes an efficient way for developing a new generation of solar cells with high efficiency and environmental stability. Using machine learning (ML) and density functional theory calculations, we report four promising inorganic photovoltaic materials—Ba4Te12Ge4, Ba8P8Ge4, Sr8P8Sn4, and Y4Te4Se2—demonstrating notable theoretical photovoltaic performance for use in solar cells. The symmetry-allowed optical transition probability, the large amount of density of states near the conduction band minimum (CBM) and the valence band maximum (VBM), and the strong p-p transition across the band edge contribute to the large optical absorption coefficient, leading to the outstanding theoretical power conversion efficiency (PCE). The separation of the VBM and CBM wave function distributions contribute to the fast separation of the photogenerated electrons and holes and the enhanced carrier lifetimes. Our ML model is an efficient method for fast and atomic-level accuracy prediction of photovoltaic materials with different crystal structures.