GDML because systems with non-covalent clusters are naturally described in terms of n-body interactions. Data-driven, many-body potentials (e.g., MB-pol) have already been widely successful in modeling aqueous systems. This expansion is formally exact if all N-body interactions are accounted for with sufficient accuracy and precision. However, the expansion is typically truncated to the third order due to combinatorics. One can avoid truncating the expansion and include all contributions by using a classical many-body polarization model (e.g., a Tholetype model as in MB-pol ). We expect training on fundamental n-body interactions found in clusters would extend GDML force fields to be useful for bulk liquid simulations. Alternative approaches exist; for example, Gaussian Approximation Potential (GAP) was extended to liquid methane by decomposing energies into fundamental interactions (e.g., repulsion, dispersion, and electrostatic contributions) and different scales. This is another rigorous approach that requires considerable effort with large numbers of quantum chemical calculations.