This work, as well as our previous papers, are part of a larger project to develop a fast computational platform for screening materials for organic photovoltaic (OPV) devices. Such a platform would enable candidate OPV materals to be screened in silico, in a similar manner to how drug candidates are screened by protein docking simulations in the pharmaceutical industry. By enabling rapid and accurate predictions of exciton coupling parameters, our framework brings our project one step foward. It might also be used for predicting coupling parameters related to charge transport. However, there are other parameters in the Marcus rate expression which we still need first-principles calculations to estimate, including the reorganization energies. Additional parameters related to electron-phonon coupling might need to be considered if other rate expressions were to be used for modeling exciton diffusion. While we will eventually need to employ machine learning to predict these other parameters as well, we increasingly feel that machine learning should be integrated into physical models, such as the ATC model used here, in order to achieve high generality and low training data requirements. We will explore this strategy further as we move forward.