Accelerating band gap prediction for solar materials using feature selection and regression techniques

•A novel machine learning approach to accelerate solar materials discovery.•Combining feature selection with regression analysis to improve bandgap modeling.•Boost bandgap prediction accuracy of over 200 chalcopyrites.•Improving previously established models by adding binary features.•Lay a framework for future studies in material property predictions.