摘要
The rapid advancement of machine learning(ML)technology across diverse domains has provided a framework for discovering and rationalising materials and photovoltaic devices.This study introduces a five-step methodology for implementing ML models in fabricating hole transport layer(HTL)free carbon-based PSCs(C-PSC).Our approach leverages various prevalentMLmodels,andwe curated a comprehensive dataset of 700 data points using SCAPS-1D simulation,encompassing variations in the thickness of the electron transport layer(ETL)and perovskite layers,along with bandgap characteristics.Our results indicate that the ANN-based ML model exhibits superior predictive accuracy for C-PSC device parameters,achieving a low root mean square error(RMSE)of 0.028 and a high R-squared value of 0.954.The novelty of this work lies in its systematic use ofMLto streamline the optimisation process,reducing the reliance on traditional trial-and-error methods and providing a deeper understanding of the interdependence of key device parameters.
基金
funded by the Engineering and Physical Sciences Research Council,UK:PhD Fellowship and Engineering and Physical Sciences Research Council,UK:EP/T025875/1.
