Most of these studies developed their models for the case of systems containing only one type of molecule. While useful for simulating exciton diffusion in large amorphous systems, it is unlikely that these models would generalize well to other types of molecules. One study which attempted to overcome this problem was reported by Li et al., which explored whether coupling parameters for charge transfer between molecules with different elements, different numbers of conjugated rings, and various spatial configurations could be modeled with machine learning. Nonetheless, it remains difficult to create a machine learning model that can predict exciton coupling parameters for a wide range of molecules. While KRR and GPR models can, in principle, be trained to fit any data set, in practice they have simple functional forms which might not be flexible enough to capture how exciton coupling varies with molecule type. Moreover, the performance of KRR and GPR models tends to scale poorly with dataset size, meaning that they might fail to discover some important trends from a large database containing more than a several thousand types of molecules. A different type of machine learning model which goes beyond the KRR and GPR framework appears necessary for predicting exciton coupling over a wide range of molecule types.