Designing donor (D) and acceptor (A) structures and discovering promising D-A combinations can effectively improve organic photovoltaic (OPV) device performance. However, to obtain excellent power conversion efficiency (PCE), the trial-and-error structural design in the infinite chemical space is time-consuming and costly. Herein, a deep learning (DL)-assisted design framework for OPV materials is proposed. To effectively digitally represent the D and A structures, a structure …