A Set of Complementary Descriptors for the Power Conversion Efficiency Predictions of Organic Solar Cells

Organic solar cells (OSCs) have undergone rapid development over the past few decades owing to their high efficiency, mechanical flexibility, and potential for low-cost large-scale fabrication. Machine learning (ML) models capable of accurately predicting the power conversion efficiency (PCE) of OSCs can greatly accelerate the discovery of high-performance donor and acceptor materials. In this work, we developed ML models for PCE prediction by introducing a set of complementary molecular descriptors and performing a comprehensive descriptor selection. The resulting models achieved the highest test set R2 value of 0.82 and average R2 value of 0.76 on an updated mixed experimental data set, representing one of the best accuracies reported to date for OSC PCE prediction. Validation using published experimental data confirmed the model’s strong predictive accuracy and generalizability. Our results highlight that the proposed OPT3D descriptors effectively complement the structural information missing from RDKit and Morgan fingerprint descriptors. This study demonstrates that the development of complementary molecular descriptors is critical for improving ML model accuracy in molecular material research and provides a promising approach for the rational design of high-efficiency OSC materials.