Efficient and Accurate Prediction of Double Perovskite Quasiparticle Band Gaps via Machine Learning and a Descriptor

Perovskites have attracted considerable attention in materials science due to their promising applications in photovoltaics and photocatalysis. Accurate prediction of their electronic band gap is essential for optimizing the performance. Traditional computational methods for band gap prediction often face a trade-off between accuracy and computational efficiency. General density functional theory (DFT) calculations typically underestimate band gap values, while the more accurate quasi-particle method demands substantial computational resources. In this study, a multistep machine learning framework was developed for efficient screening of semiconductor double perovskites. Furthermore, we proposed an interpretable descriptor that can predict quasi-particle band gaps of perovskites with a precision of over 90% accuracy. Using this approach, we screened 4,507 perovskite candidates and identified 94 structures that have suitable band gaps and are lead-free. Among these, six candidate structures were selected for further verification based on their photocatalytic potential and thermal stability.