Machine learning is now a well-established as a means for overcoming the time-and cost-demands of firstprinciples calculations. It has found numerous applications in organic semiconductor research, including for band gap prediction, reorganization energy prediction, and others. An important parameter for exciton diffusion modeling are the exciton coupling parameters, which appear as coefficients in the Marcus hopping rate equation and are usually computed using time-dependent density functional theory (TDDFT). Several groups have succeeded to predict exciton coupling parameters using machine learning. Kernel ridge regression models (KRR) for exciton coupling parameters have been reported by Lederar et al., Wang et Two-step schemes involving kernel ridge and Gaussian pro-cess regression (GPR) have been reported by Lin et al. as well as us. Other models for predicting exciton coupling, including neural networks and random forests, have been reported by Miller et al., Çaylak et al., Wang et al., and Farahvash et al. The exciton coupling parameters predicted by these models compare well to TDDFT reference data, and in many cases lead to accurate estimates of exciton diffusion coefficient when used subsequently in simulations.