Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning

The discovery of stable, high-performance lead-free hybrid perovskite materials remains a major challenge for next-generation photovoltaic technologies. Here, a high-throughput computational framework integrating machine learning, deep learning, and density functional theory (DFT) calculations was adopted to accelerate the discovery and validation of lead-free hybrid perovskites. An optimized XGBoost model was firstly employed to screen approximately 180k charge-neutral perovskite candidates according to formation-energy and bandgap criteria. The resulting 48 kinds of Ge-based candidates were subsequently validated using CGCNN and UPET models, leading to the identification of (CH)₄NH₂GeGeCl₆, C₃H₄NSGeGeBr₆, C₅NH₁₂GeGeBr₆ and C₆OH₁₃GeGeBr₆ as promising photovoltaic materials, with HSE + SOC-calculated bandgaps of 1.5137, 1.7515, 1.6778 and 1.1680 eV, respectively. These results demonstrate that integrating data-driven prediction with first-principles validation provides an effective strategy for accelerating the discovery of next-generation photovoltaic materials.