We now discuss mbGDML data, which approximates the MBE potential energy surface. In general, mbGDML reasonably mimics MBE data, including innate errors made by the MBE framework, as seen in the acetonitrile 5mer and 6mer data. Note that fortuitous error cancellations of 2-and 3-body mbGDML predictions sometimes give the appearance of higher accuracy than MBE. mb-GAP and mbSchNet potentials occasionally are better or worse than mbGDML; however, as previously mentioned, these methods generally require larger training sets and are likely to underperform. For example, Table shows the MBE, mbGDML, mbGAP, and mbSchNet energy and force mean absolute errors (MAEs) with respect to supersystem MP2/def2-TZVP calculations for all 4-6mer structures considered here. All mbML models perform similarly for water and acetonitrile, but the methanol isomer errors Their 2-body training set included 34 431 structures containing the global dimer minimum, saddle points, artificially compressed geometries, and geometries from path-integral molecular dynamics (PIMD) simulations using HBB2-pol. Their 3-body training set contained 10 001 structures from HBB2-pol MD and PIMD of small water clusters, liquid water, and ice phases. Table shows 2-and 3-body interaction energy MAEs with their models and those calculated here. The PIP, BPNN, and GAP water models trained on large We reiterate that ML potential accuracy is intricately linked to reference data sets, but we specifically opted to show the promise of GDML with small training sets. In almost all cases, the water 2body models prepared here outperformed those from Ref. 53 that used larger training sets. Presumably, our smaller data sets may contain structures that enhance the perceived accuracy of these models. The 3-body data exhibit the opposite trend, which can be attributed to data set quality. In general, additional sampling of configurational spaces would improve our mbML models; however, the objective here is to evaluate ML potentials that can be trained with minimal computational cost.