ML methods are usually trained to QM reference calculations, which could under-perform classical molecular forcefields (MM) simulation of large systems, because the latter ones could be fit to recover experimental properties such as densities, heats of vaporization, and phase transition temperatures. In addition, the scalability of ML methods could be limited due to the use of local descriptors for representing molecules. For example, in the case of the ANI neural networks family, that has been extremely successful for describing small organic molecules, the Cartesian coordinates of the system are first transformed to heavily modified Behler-Parrinello symmetry functions that are calculated for each atom using a 5 Å cutoff.