Intelligent engineering of Cs2AgBi0.75Sb0.25Br6 lead-free perovskite solar cells via machine learning-based optimization of charge transport materials and fabrication parameters.

This study presents a machine learning-driven framework to optimize environmentally friendly lead-free Cs2AgBi0.75Sb0.25Br6 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/Cs2AgBi0.75Sb0.25Br6/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.