Developing a predictive model for the maximum power conversion efficiency of inorganic perovskites: A combined approach using density functional theory and machine learning

To further improve the applicability of perovskite materials in photovoltaics, exploring perovskites with appropriate band gaps and enhanced stability is essential. Nevertheless, identifying promising perovskite materials through a perennial trial-and-error approach is both time-consuming and expensive. In this study, we introduce a method that combines machine learning (ML) and density functional theory (DFT) calculations to efficiently screen inorganic perovskite materials for photovoltaic applications. By utilizing 107 experimental data, we built a machine learning regression model capable of predicting the maximum power conversion efficiency (PCE) achieved in experiments. Light Gradient Boosting Machine (Lightgbm) exhibited superior performance with a test set R² score of 0.89. Simultaneously, another machine learning regression model was trained using 405 data to predict the theoretical maximum PCE. The best-performing model was Extreme Gradient Boosting (Xgboost) with a test set R² score of 0.93. By integrating these ML models with DFT calculations, we identified three potential inorganic perovskites: CsPdCl₃, KGeCl₃, and CsCu₂Br₃. These materials exhibit direct bandgaps of 1.47 eV, 1.37 eV, and 1.65 eV respectively, along with high thermal stability and favorable optical properties. This method constructs an experimental-theoretical-data driven framework for the prediction of inorganic perovskites, effectively reducing the research cycle in perovskite photovoltaics.