Enhancement of sulfide-based absorber and charge transport layer solar cell performance using machine learning and the SCAPS-1D simulator

, and 89.10%, respectively. We subsequently developed a machine learning (ML) model to predict the output parameters of the photo devices. Using ML, the performance matrix of the photocells under study was predicted with an accuracy rate of 83.75%. The study sheds light on this important field and provides a workable method for constructing cost-effective PbS-based photovoltaic cells.