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.

发现新的无机光伏材料成为开发具有高效率和环境稳定性的新一代太阳能电池的有效途径。使用机器学习（ML）和密度泛函理论计算，我们报告了四种有希望的无机光伏材料-Ba ₄ Te ₁₂ Ge ₄，Ba ₈ P ₈ Ge ₄，Sr ₈ P ₈ Sn ₄和Y ₄ Te ₄ Se ₂-展示了用于太阳能电池的显着理论光伏性能。对称性允许的光跃迁几率，导带最小值（CBM）和价带最大值（VBM）附近的大量状态密度以及整个能带边缘的强pp跃迁有助于大的光吸收系数，导致达到出色的理论功率转换效率（PCE）。VBM和CBM波函数分布的分离有助于光生电子和空穴的快速分离以及提高的载流子寿命。我们的ML模型是一种有效的方法，可以快速和原子级地预测具有不同晶体结构的光伏材料的精度。