Machine Learning-Assisted Optimization of Cu-Based HTLs for Lead-Free Sr3PBr3 Perovskite Solar Cells Achieving Over 30% Efficiency via SCAPS-1D Simulation

This ZIP archive contains data, figures, and analyses related to the development and optimization of Sr₃PBr₃-based solar cells incorporating various Cu-based inorganic hole transport layers (HTLs). The contents support simulation-driven insights into device architecture, material parameters, interface engineering, and machine learning-based performance prediction. Sections included: Novel Sr₃PBr₃ solar cells without HTL, with various Cu-based HTLs, and with optimized HTL Band configuration of Sr₃PBr₃-based photovoltaic cells Impact of varying absorber thickness and doping density in Sr₃PBr₃ Contour plots illustrating effects of active layer thickness and defect density on performance metrics Effect of varying SnS₂ layer thickness and donor concentration Impact of interface defect density at the Sr₃PBr₃/CBTS interface Combined effect of interface defect density and absorber thickness Influence of series and shunt resistance with and without CBTS HTL Temperature-dependent behavior of device performance J–V characteristics and Quantum Efficiency of the proposed cell Machine learning-based analysis using Random Forest and SHAP values for PCE prediction.