Thus, mbGDML should provide size-transferable force fields trained on highly accurate quantum chemical methods. To evaluate this, we developed an automated framework in Python to facilitate training and application of mbGDML force fields (available at github.com/keithgroup/mbGDML). GDML force fields for water (H 2 O), acetonitrile (MeCN), and methanol (MeOH) were trained on 1000 structures for 1-, 2-, and 3-body interactions. GAP and SchNet were also evaluated in this many-body framework. The size transferability of mbGDML was further assessed against a highly promising graph neural network, Neural Equivariant Interatomic Potentials (NequIP). Reference structures from the literature were used to benchmark energy and forces predictions. The following sections demonstrate mbGDML energy and force accuracies within 0.38 kcal/mol per monomer and 0.06 kcal/(mol Å) per atom for structures containing up to 20 monomers (120 atoms). The MBE framework itself contributes 14% to 83% of these errors depending on the system. Error cancellation dramatically improves relative energy predictions of mbGDML to less than 3 kcal/mol and achieves fair to excellent agreement with solvent radial distribution functions (RDFs).