Intelligent interface engineering of electron and hole transport layers for BeSiP2 solar cells

Abstract The development of lead-free and environmentally benign absorbers is a critical step toward sustainable solar technologies. This study investigates the optoelectronic behavior and performance optimization of beryllium silicon diphosphide (BeSiP 2 ) based thin-film solar cells using SCAPS-1D simulation coupled with machine learning (ML) analysis. Various electron transport layers (ETLs) PCBM, SnO 2 , WS 2 , TiO 2 and hole transport layers (HTLs) V 2 O 5 , CuI, CuSCN, NiO were examined to determine the optimal device configuration. The optimized FTO/PCBM/BeSiP 2 /V 2 O 5 /Cu–C device architecture achieved a high-power conversion efficiency (PCE) of 28.12%, with open circuit voltage (V oc ) = 1.13 V, Short Circuit Current Density (J sc ) = 29 mA cm⁻², and Fill Factor (FF) = 88%. Numerical analysis indicates that thin transport layers (50–100 nm), moderate doping concentrations (10 17 –10 18 cm −3 ), and low interface defect densities (≤ 10 14 cm −3 ) are critical in minimizing recombination losses and achieving this efficiency. To complement the numerical analysis, an ML framework utilizing eight regression algorithms was implemented to predict key photovoltaic parameters. Gradient Boosting (GB) achieved the highest accuracy (R 2 > 0.98), identifying shunt resistance (R sh ) and irradiance as dominant factors influencing device metrics. The hybrid SCAPS-ML approach effectively bridges physics-based and data-driven insights, enabling rapid, interpretable optimization of lead-free chalcopyrite solar cells.