Machine Learning-Assisted Optimization of Rb2LiGaI6-Based Perovskite Solar Cells: Insights from SCAPS-1D Simulation

Abstract The development of high-efficiency and stable per- ovskite solar cells (PSCs) requires both suitable material selection and advanced predictive modeling. In this study, Rb 2 LiGaI 6 , a halide perovskite with a bandgap of 1.13 eV, is explored as the absorber material due to its high absorption coefficient ( > 105 cm −1 ), low carrier effective masses (∼0.2–0.4 m 0), and thermodynamic stability. Device architecture optimization was performed using a machine learning (ML) framework to predict photovoltaic performance metrics, including J sc , V oc , FF, and PCE, under varying ETL/HTL interface configurations. Among the tested models, Random Forest achieved the best predictive accuracy R 2 of 0.9974 surpassing XGBoost, HistGBR, and KNN. Correlation and SHAP analyses revealed that HTL bandgap, conduction band offset, electron affinity, and hole mobility play critical roles in governing efficiency. Device simulations showed that absorber thickness strongly influences PCE, to a maximum of 22% at 1000 nm which is very close approximation of the ML predicted PCE of 22.13% with only a prediction error of 0.6%. Optimization of transport layers demonstrated that a thin ETL (∼50 nm) avoids parasitic absorption losses, while moderate HTL thickness (∼100–150 nm) enhances V oc . Temperature- dependent studies further confirmed that device efficiency drops drastically due to elevated dark current. These findings highlight the potential of combining Rb 2 LiGaI 6 absorbers with ML-guided optimization to design efficient and stable PSC architectures.