Part of the reason that a highly generalizable machine- learning model for exciton coupling has not yet been created is that the data sets must consist of pairs of molecules (dimers), rather than single molecules. For the case of dimers, exciton coupling does not just depend upon molecule type, but also depends upon the relative orientation of the two molecules. Thus, an enormous dataset which contains not only a large number of molecule types, but also a large number of relative orientations between pairs of those molecules, is required. Such an enormous dataset might not be feasible to generate. In addition to these size requirements, the standard method for computing exciton couplings -computing a two-body integral over the molecule's transition charge density (the electronic energy transfer (EET) method, Figure ) -is very time-consuming. However, there is a solution to this problem which has not yet been explored: the atomic transition charge (ATC) method (Figure ). In the ATC method, a transition charge is first assigned to each atom on the basis of the molecule's transition density. The exciton coupling V ab between a pair of molecules a and b is then calculated by summing a series of simple Coulomb-like terms: