Machine learning and SCAPS-1D based prediction and validation of RbGeBr3 perovskite solar cell

This study predicts the solar cell performance of RbGeBr3 perovskite using Machine Learning (ML) models and validated via the solar capacitance simulator (SCAPS-1D). A DFT-derived data set from the MaterialsZone repository, containing organic–inorganic halide perovskites data, was analyzed using Scikit-learn-based models and association rule mining. A total of 443 solar cell configurations were evaluated using nine key input features to predict power conversion efficiency (PCE). Among various models which includes Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gradient Boosting Regression (GBR), and Extreme Gradient Boost (XGB) -the XGB model performed best. Hyperparameter tuning via improved model accuracy predicting a PCE of 30.67 % for the solar cell using the XGB model shows 1.13 % of test root mean square error (RMSE) and 0.877 of test correlation coefficient (R2) for Al/FTO/SnS2/RbGeBr3/P3HT/Ni device structure. SCAPS-1D validation closely matched ML predictions, achieving PCE of 31.76 %. These findings confirm the potentiality of RbGeBr3 for highly efficient perovskite solar cell (PSC), underscoring the synergy between ML-based screening and theoretical modeling for the design of next-generation PSCs. © 2025 International Solar Energy Society