Numerical optimization and machine learning analysis of chalcogenide based perovskite solar cells via absorber layer engineering using SCAPS-1D

This paper presents a comparative numerical analysis of eco-friendly chalcogenide perovskite solar cells using three Barium zirconium sulfide derived absorber materials: BaZrS 3 , Ba(Zr 0.95 Ti 0.05 )S 3 and BaZr(S 0.6 Se 0.4 ) 3 , with SCAPS-1D. The simulated device structure is FTO/CdS/absorber/PEDOT: PSS/Ir, with PEDOT: PSS used as the hole transport layer due to its matching energy levels and its ability to effectively extract holes. The impact of absorber composition on photovoltaic performance, focusing on bandgap tuning, charge transport and recombination properties, was systematically analyzed. Of the investigated absorber variants, Ti-substituted device Ba(Zr 0.95 Ti 0.05 )S 3 was found to be the most successful one with the highest power conversion efficiency of 25.89% and an open circuit voltage (Voc) of 1.280 V, short circuit current density (Jsc) of 24.59 mA/cm 2 and a fill factor (FF) of 82.22%. This is due to the improved performance enabled by better optoelectronic properties, better band alignment, and reduced carrier recombination resulting from Ti incorporation. The results here imply that compositional design of BaZrS 3 -based absorbers is a promising strategy for improving the performance and stability of lead-free PSCs. The device’s performance was also optimised using a random forest model, accounting for the effects of thickness, temperature, bulk defect density, and interface defect density. This hybrid simulation-ML model effectively enabled predicting the main performance tendencies and optimising the parameters.