Intelligent engineering of Cs₂AgBi_0.75Sb_0.25Br₆ lead-free perovskite solar cells via machine learning-based...

This study presents a machine learning-driven framework to optimize environmentally friendly lead-free Cs₂AgBi_0.75Sb_0.25Br₆ perovskite solar cells (PSCs) by focusing on charge transport layer selection and fabrication parameter tuning. We integrate large-scale SCAPS-1D simulations with a computationally efficient machine learning (ML) model to systematically explore 25 ETL-HTL combinations and identify the optimal device configuration among all 25 devices. We generate a large dataset of 20 935 samples by varying key parameters, including ETL thickness (T _E), absorber thickness (T _A), HTL thickness (T _H), donor density (N _D), acceptor density (N _A), and absorber defect density (N _Ad), along with charge transport layer materials. This dataset is then used to train ML models. Among the 15 evaluated models, XGBoost achieves the best performance with R ² = 0.9996 and RMSE = 0.0752 (75 : 25 train-test split). We optimize the devices using differential evolution, and the ZnOS/Cs₂AgBi_0.75Sb_0.25Br₆/CFTS configuration achieves the highest power conversion efficiency, increasing from 22.93% to 25.95%, which closely matches the ML-predicted value of 26.03%. This improvement results from enhanced charge transport, an increase in built-in potential (V _bi) from 0.518 to 0.533 V, re-duction in ideality factor from 1.984 to 1.945, an increase in activation energy from 1.388 to 1.391 eV, reduced recombination losses and improved quantum efficiency. This study establishes a robust data-driven paradigm for accelerating the development of high-performance, lead-free PSCs.