Performance Boost in Novel CBAI/SNMO Double Perovskite Solar Cells via GO-Induced Back Surface Field Optimization

Abstract

This investigation presents a comprehensive SCAPS-1D and machine-learning-assisted investigation of four environmentally benign double-perovskite solar cell architectures: Device-I (Cs 2 BiAgI 6 /WS 2 /FTO), Device-II (Sm 2 NiMnO 6 /WS 2 /FTO), Device-III (Sm 2 NiMnO 6 / Cs 2 BiAgI 6 /WS₂/FTO), and Device-IV (GO/Sm 2 NiMnO 6 /Cs 2 BiAgI 6 /WS 2 /FTO). Systematic simulation demonstrates that integrating both Sm 2 NiMnO 6 and Cs 2 BiAgI 6 into a dual-absorber configuration (Device-III) significantly improves light harvesting, band alignment, and charge separation compared to the single-absorber reference devices. The inclusion of a graphene oxide (GO) back-surface-field layer in Device-IV further suppresses interfacial recombination, boosts hole reflection, and strengthens the built-in electric field, enabling the highest performance with V OC = 1.16 V, J SC = 35.72 mA/cm², FF = 79.62%, and PCE = 32.99%. The study reveals that absorber defect density, acceptor concentration, and interface trap states strongly dictate recombination losses across all structures. To accelerate optimization, ensemble machinelearning models (Random Forest, Gradient Boosting, and XGBoost) trained on extensive SCAPS-generated datasets achieve excellent prediction accuracy (R² > 0.98), while SHAP interpretability identifies absorber defect density and electron affinity as the dominant parameters influencing device efficiency. The combined physics-based and data-driven workflow provides clear, quantitative design rules for high-performance, eco-friendly doubleperovskite solar cells and establishes a versatile path toward scaling next-generation, ecofriendly photovoltaic technologies.