Several aspects of our work warrant further discussion. While machine-learned models are obviously faster at making predictions than first-principles methods, they can only be built after a set of training data has been obtained. For the case of neural network models, training data size requirements are usually very large. Neural network models in chemistry and chemical physics typically require datasets containing 10,000 to 100,000 examples of molecules or materials, as well as their DFT-calculated properties. Generating such massive training sets is computationally very costly. Taking this into account, the training data requirements for our framework are surprisingly light. While our final training data set consisted of 99,784 atom examples and their DFT-calculated ATC values, this data was generated by performing DFT calculations on only 1843 molecules. The computational demands for generating our training data are therefore comparable to those of simple kernelized models such as kernel ridge regression and Gaussian process regression, for which data sizes in the order of a few thousand are usually acceptable. In a sense, our framework provides access to neural network-levels of accuracy at the price of a kernelized model. These reduced data size requirements are, of course, due to the use of the ATC approximation; in fact, our GNN does not predict exciton cou-pling directly, but only the parameters of a coupling model. It would be interesting to see whether this strategy could be applied in other ways, such as in predicting transition dipole moments.