While our framework performs well across a wide range of molecules, it has trouble when applied to cases with very small exciton couplings. This shortcoming is not expected to be an issue when using our framework to simulate exciton diffusion in important and topical organic semiconductor materials such as FREAs. Such materials are already known or are expected to display good exciton transport properties, and hence show large excitonic coupling parameters. However, this shortcoming could become serious if our framework was used for high-throughput screening purposes, in which many candidate materials would be screened on the basis of excitonic coupling or exciton diffusion coefficient sizes. In this situation, our framework may over-estimate exciton couplings for materials which in fact have very weak couplings, raising the false-positive rate of the screening process. We have encountered similar problems previously when trying to predict weak intermolecular interactions and weak exciton couplings using Gaussian process regression models. In these works, we dealt with the problem by using a split scheme in which a support vector machine (SVM) directs the input to one of two different regression models for prediction on the basis of expected interaction or coupling strength. These regression models in turn were trained exclusively using data for the weak and strong cases, respectively. Such an SVM would be trained using 0/1-labelled data, where 0 and 1 would correspond to weak and strong coupling, respectively. Such classi-fication models are usually easier to train than regression models. In our case, the SVM would be followed by one of two separate graph neural network models, respectively trained on the weak-and strong-coupling cases. This split scheme has an additional benefit in that it reduces overall training data size requirements. Future development of our framework might therefore investigate whether a similar splitting scheme could be introduced into a graph neural network model, particularly if high-throughput virtual screening is of interest.