Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells

Abstract

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 prevalent ML models, and we 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 of ML to 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.