To demonstrate the performance of OCNet in transport-related tasks, we utilize OCNet's bimolecular representations to predict intermolecular electronic couplings (transfer integrals) in both crystalline and film environments. We first evaluate the performance of OCNet on the OCELOT dimer dataset, 13,46 a diverse dataset containing 438,000 DFT-derived transfer integrals from about 25,000 molecular crystal structures. Inspired by the work of Valeev et al., we select domain features including the distance between molecular centroids, the angle between molecular plane normal vectors, and the angle between the centroid-to-centroid vector and the molecular plane normal vectors. Subsequently, we demonstrate the correlation between the predictions of the domain feature-integrated OCNet and the corresponding QM calculation results. Leveraging pre-training on bimolecular conformations and TB level transfer integrals, we achieved accurate predictions of H-H and L-L transfer integrals. in the OCELOT dimer dataset, with MAEs of 0.058 eV and 0.061 eV, respectively, and R 2 values of 0.909 for both cases. We also compare the accuracy of OCNet with reported SOTA model in predicting H-H and L-L transfer integrals (Figure , Tables and).