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.