The primary goal when training DFAs is to learn how a given electronic density translates into energy. However, ML practices in QC currently treat energies as not very informative, following a 1 system = 1 energy data point approach (see Refs. 28-30, 32, 35, 37, 53, 57, and 58). For example, when learning force fields, forces (energy gradients) are much more informative than energies . Similarly, recent works have shown that in current ML DFA practices, electronic densities are also far more informative than energies (each grid point is a density datapoint). The use of electronic densities effectively enhances the transferability of ML DFT, yet when it comes to energies, all these works still adhere to the 1 system = 1 energy data point approach. The key question of the present work is therefore: