Deep insights into lead-free Sr3BiI3-based anti-perovskite solar cells: optimization strategies and impedance spectroscopy via numerical simulation and machine learning

Abstract

Anti-perovskite solar cells (APSCs) are garnering substantial attention due to their promising potential in the renewable energy sector and their distinctive characteristics. This research investigates the structural, optical, and electrical properties of Sr₃BiI₃ using Density Functional Theory (DFT) and further evaluates its photovoltaic (PV) performance in Sr₃BiI₃-based lead-free APSCs through the SCAPS-1D simulator. The device architectures employ Sr₃BiI₃ absorbers and explore the impact of different electron transport layers (ETLs) including WS₂, IGZO, PCBM, and SnS₂, alongside with the multiple hole transport layer (HTL). The impacts of layer thickness, defect density, doping concentration, series and shunt resistances, operating temperature, and impedance response (evaluated through Nyquist plots) are systematically examined for all ETLs. The power conversion efficiency (PCE) of devices optimized with WS₂, IGZO, PCBM, and SnS₂ as ETL and CBTS as HTL were determined to be 30.20%, 30.12%, 29.18%, and 30.31%, respectively. To further improve device optimization, machine learning models, particularly Random Forest, were trained on SCAPS-1D simulation results. The application of machine learning models reduces experimental duration and eliminates the need for extensive resources in the design and prediction of PV performance in solar cells. The model forecasts performance with an impressive correlation coefficient (R²) of 0.989 for PCE. To enhance interpretability, methods such as correlation heatmaps, feature importance, and SHAP analysis were employed to assess the impact of critical parameters on device efficiency. The suggested framework provides a reliable and efficient method for forecasting the most significant factors and their influence on the performance of Sr₃BiI₃-based APSCs.