Ideally, with access to a robust data generator for the exact e c (r), we could use LES to train e MLS2 c (r). However, due to the severe computational limitations of such a generator to very small systems and basis sets, we settle with a GES based training of MLS2 (the resulting loss function is detailed in Sec. S4 in the SI). Using GES here requires more data for training. Thus, we employ the eight atoms/ions training dataset from ML2 (see Sec. III) combined with 13 small closed-shell molecules, mainly dimers, and additionally H 2 , N 2 and Li 2 at five geometries of large interatomic distances. In addition to these total energies, our MLS2 NN is also trained on interaction energies from the RG18 dataset , including dispersion-bound complexes with noble gases. We elaborate in Sec. S4 of the SI on how we combine the total energy GES and the interaction energy GES when training MLS2. Furthermore, a detailed MLS2's list of all training data points is given in Sec. S8 of the SI.