Predicting photovoltaic parameters of perovskite solar cells using machine learning

Perovskite solar cells (PSCs) have garnered significant attention owing to their highly power conversion efficiency (PCE) and cost-effectiveness. Traditionally, screening for PSCs with superior photovoltaic parameters relies on resource-intensive trial-and-error experiments. Nowadays, time-saving machine learning (ML) techniques serve as an artificial intelligence approach to expedite the prediction of photovoltaic parameters using accumulated research datasets. In this study, we employ seven supervised ML methods to forecast key photovoltaic parameters for PSCs such as PCE, short-circuit current density (