Exploring optoelectronic and photovoltaic properties of Be3MF3 (M = P, As, Sb) perovskites via machine learning and numerical simulation

This study presents a comprehensive Density Functional Theory (DFT) analysis of the structural, mechanical, electrical, and optical characteristics of cubic Be₃MF₃ (M = P, As, Sb) perovskites, highlighting its potential for optoelectronic applications. The computed elastic constants adhere to the Born stability criterion, affirming mechanical resilience and ductility. Analyses of band structure and density of states reveal that all Be₃MF₃ compounds exhibit indirect band gaps between 2.0 and 3.3 eV, categorizing them as wide-bandgap semiconductors suitable for high-power and UV optoelectronic applications. Optical investigations demonstrate significant absorption, especially in Be₃SbF₃, highlighting its promise as a lead-free absorbing material for solar cell applications. Device-level performance was modeled utilizing SCAPS-1D, incorporating Be₃SbF₃ as the absorber across diverse configurations with four electron transport layers (In₂S₃, TiO₂, Mg:ZnO, IGZO) and six-hole transport layers (Cu₂O, MoO₃, P3HT, NiO, PTAA, V₂O₅). The best configuration, Al/FTO/In₂S₃/Be₃SbF₃/Cu₂O/Ni, attained a maximum power conversion efficiency (PCE) of 18.28%, with open-circuit voltage (V_OC) of 1.79 V, short-circuit current (J_SC) of 11.04 mA/cm², and a fill factor (FF) of 89.6%. Subsequent examinations of defect density, interface states, thickness variation, and temperature impacts clarify charge transport and recombination kinetics. A Random Forest machine learning (ML) model was created to forecast device performance, with high accuracy (R² = 0.987, MSE = 0.00305, MAPE = 0.075) and pinpointing bandgap energy and interface defect density as critical limiting factors. The integrated DFT-SCAPS 1D-ML framework identifies Be₃SbF₃ perovskites as viable, lead-free materials for advanced photovoltaic and optoelectronic technologies.