High throughput molecular design of electron donors and non-fullerene acceptors using machine learning combined with substructure importance

In this study, we constructed a database to train a random forest model, identified key substructures through feature importance analysis, and designed and performed virtual screening for high-performance novel donor–acceptor molecules.