Machine-learning-assisted prediction of the power conversion efficiencies of non-fullerene organic solar cells

We create a new dataset composed of over 1500 non-fullerene organic solar cells (NF-OSCs) by curating experimental data from recently published literature. The dataset includes performance metrics such as power conversion efficiencies (PCEs), short-circuit currents, open-circuit voltages, and fill factors of the NF-OSCs, together with chemical structures of donor/acceptor pairs and device fabrication conditions, which have been reported to have a significant influence on the device performance. Additionally, we conduct quantum chemical calculations of donor/acceptor molecules present in the dataset to obtain their electrochemical properties. We construct several features reflecting chemical structures, electrochemical properties, and device fabrication conditions, which are subsequently fed into a kernel ridge regression model to predict the PCEs of the NF-OSCs. The prediction results indicate that the structural feature alone is insufficient for reliable prediction, while concatenating structural and electronic features significantly improves the prediction performance. We also examine the prediction performance for out-of-sample NF-OSCs containing Y6-type acceptors, highlighting reasonable efficacy of the concatenated features.