Deep Learning‐Assisted Design of Novel Donor–Acceptor Combinations for Organic Photovoltaic Materials with Enhanced Efficiency

Abstract 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 representation method, polymer fingerprints, is developed, and a database of OPV materials is constructed. By applying an end‐to‐end graph neural network modeling method, high‐precision DL models for predicting OPV performance are established. After combining the existing structures, ≈0.6 million virtual D‐A combinations are generated. Then, the OPV performance of these candidate combinations is predicted by the well‐trained models, and numbers of novel D‐A combinations with high efficiency are identified. Experimental validations confirm that the prediction accuracy is greater than 93% and one of the screened combinations (i.e., D18:BTP‐S11) exhibits an efficiency above 19.3% in single‐junction organic solar cells. Finally, based on the structural gene analysis, the design rules to guide experimental explorations are suggested. The developed DL‐assisted approach can accelerate the design of D‐A combinations with ultrahigh efficiency and bring property breakthroughs for OPV devices.