Machine learning-assisted optimization of CsPbI₃-based all-inorganic perovskite solar cells: A combined SCAPS-1D and XGBoost approach
Usama Ghulam Mustafa, Wei Wu, Mingqing Wang, Adham Hashibon, Hafeez Anwar
Abstract
ABSTRACT The commercialization of perovskite solar cells (PSCs) is hindered by the instability of organic components and the resource-intensive nature of experimental optimization. Machine learning (ML) is revolutionizing the discovery and optimization of photovoltaic devices by reducing reliance on conventional trial-and-error approaches. This study aims to optimize the performance of CsPbI₃-based all-inorganic PSCs using a combined SCAPS-1D and machine learning (ML) approach. We generated 56,390 unique device configurations via SCAPS-1D simulations, varying layer thicknesses and defect densities. Five ML models were trained, with XGBoost achieving the highest accuracy (R² = 0.999). Feature importance was analyzed using SHAP. Optimization increased the PCE from 15.15% to 19.16%, with the perovskite layer thickness (2 µm) and defect density (<10¹⁵ cm⁻³) identified as critical parameters. This study highlights the potential of ML-driven optimization in perovskite solar cells, offering a systematic and data-driven approach to enhancing device efficiency and accelerating the development of next-generation photovoltaics.