Graph-Based Nitrogen Substitution and CatBoost Regression for Virtual Screening of Small-Molecule Acceptors in Organic Photovoltaics.

Machine learning-driven high-throughput screening strategy was used for designing efficient nitrogen-containing small-molecule acceptors (SMAs) for organic solar cells. First, a database containing 1027 experimentally determined SMAs was encoded using RDKit molecular descriptors with the application of variance filter, correlation filter, and consensus feature selection. Then, 11 different regression algorithms were assessed in terms of PCE prediction; the best results were obtained by CatBoost algorithm. Afterward, a graph-based nitrogen substitution approach was implemented to design about 200 K chemically valid mono-, di-, and tri-nitrogen-containing SMA candidates. Further virtual screening allowed us to identify several promising candidates possessing predicted PCE value up to 16.42%. The structural analysis of chemical space revealed that all candidates are diverse and suitable for further investigation.