A Transferable Descriptor for Accurate Prediction of Excitonic Couplings in A-D-A Organic Photovoltaic Acceptors

Exciton diffusion length (LD) is a key parameter determining the power conversion efficiency of organic solar cells (OSCs), yet its fast and accurate calculation is computationally challenging due to thin-film structural disorder and the long-range nature of excitonic coupling. Herein, we introduce a coarse-grained Coulomb matrix (CG-CM) descriptor that enables rapid and accurate prediction of excitonic couplings. By mapping molecular conjugated backbones onto a fixed number of beads, our descriptor eliminates the dimensionality dependence on the number of atoms, allowing the machine learning model to generalize across molecules with different sizes and chemical structures. The model achieves high accuracy with a coefficient of determination (R2) exceeding 0.92 across diverse nonfullerene acceptor systems, including Y-type and ITIC-type materials. We further demonstrate the model's reliability in capturing excitonic coupling fluctuations, predicting LD in disordered thin films, and reproducing LD anisotropy in crystalline systems. This work provides a powerful and efficient tool for high-throughput screening of high-performance organic photovoltaic materials.