Predictive Modeling and Design of Organic Solar Cells: A Data-Driven Approach for Material Innovation

Abstract

We present a robust machine learning methodology to accurately predict key photovoltaic parameters in organic solar cells (OSCs). Our approach involves curating a comprehensive quantum mechanical database of 300 experimentally validated OSC devices with distinct donor/acceptor combinations. Through a two-step screening process, we identify descriptors correlated with crucial properties such as short-circuit current (J SC ), opencircuit voltage (V OC ), fill-factor (FF), and power conversion efficiency (PCE max ). Utilizing a LASSO model for feature selection and four different supervised machine learning techniques for prediction, our model achieves high accuracy, with gradient boosting showing exceptional performance for J SC , V OC , and PCE max . Shapley additive explanations (SHAP) analysis reveals the influential features and the intricate nonlinear relationships governing OSC performance. Additionally, we extend our model's utility by predicting photovoltaic parameters for a larger data set of 4680 donor−acceptor combinations, including 120 newly designed nonfullerene acceptors and 39 experimentally known donor polymers. Our results highlight 18 donor−acceptor combinations with a power conversion efficiency exceeding 15%, emphasizing the efficacy of our approach in evaluating OSC materials. This work provides valuable insights for advancing photovoltaic research and serves as a powerful tool for the virtual screening of promising donor/acceptor pairs, accelerating the development of high-performance OSC materials and devices.