Machine Learning‐Based Optimization and Performance Enhancement of CH₃NH₃SnBr₃ Perovskite Solar Cells with Different Charge Transport Materials Using SCAPS‐1D and wxAMPS

Abstract Recent research focuses on enhancing the sustainability of perovskite solar cells (PSCs) by substituting lead with non‐toxic materials, identifying tin‐based perovskites such as CH 3 NH 3 SnBr 3 as a viable alternative. This study examines the efficacy of CH 3 NH 3 SnBr 3 as the absorber layer in conjunction with V 2 O 5 as the hole transport layer (HTL) and several electron transport layers (ETLs), including C 60 , IGZO, WS 2 , and ZnSe. The study employs SCAPS‐1D simulations to optimize parameters including doping concentration, thickness, and defect density, aiming to improve photovoltaic efficiency. The optimal configuration (FTO/WS 2 /CH 3 NH 3 SnBr 3 /V 2 O 5 /Au) attained a power conversion efficiency (PCE) of 33.54%, surpassing alternative ETL combinations. The results of the SCAPS‐1D simulation are analyzed in comparison to those of the wxAMPS simulation. The machine learning model is developed to predict solar cell performance, achieving an accuracy of 82%. The findings underscore the significance of choosing appropriate ETL to enhance PSC efficiency and sustainability.