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 disco...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(R^(2)=0.999).Feature importance was analyzed using SHAP.Optimization increased the PCE from 15.15%to 19.16%,with the perovskite layer thickness(2μm)and defect density(<10^(15)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.展开更多
基金supported by the EU Horizon2020 Project Marketplace,No.760173.
文摘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(R^(2)=0.999).Feature importance was analyzed using SHAP.Optimization increased the PCE from 15.15%to 19.16%,with the perovskite layer thickness(2μm)and defect density(<10^(15)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.
