We suspect the differences in atomic charges is much less important to model than the difference in energies, as the electron density tends to be similar between different density functionals, and the partial charges are more dependent on the partitioning scheme. Since the energy predicted by our models is dependent on the predicted atomic partial charges, it is possible to backpropagate through the whole model and train both of the atomic NNs to energies alone. However, unless the training set contains many examples of atomic pairs with distances greater than the NN cutoff the predicted charges may not produce accurate long range interaction energies. Training to dipole moments aids in giving correct long range electrostatic interactions and helps to refine the charge predictions.