As previously mentioned, many ML potentials use local descriptors for size transferability. Recent developments of ML potentials with local descriptors have involved GNNs. NequIP uses equivariant, continuous convolutions where edges connect every atom within a cutoff radius. NequIP has achieved remarkable accuracy and data efficiency on the MD17 data set, bulk water, formate dehydrogenation, and amorphous lithium phosphate. Such models are inherently size transferable, but the accuracy is not typically studied when trained exclusively on small clusters. In theory, these potentials can train on the same data sets, but instead of n-body interactions, they would use total energies and forces. This would eliminate the need for an MBE framework. We trained NequIP on total energies and forces of 1000 trimers for water, acetonitrile, and methanol to assess this approach. Another We emphasize that this is an edge case of GNN potentials. If energies and forces of larger structures were readily available, these data would improve size transferability if they were included in the validation set. However, mbGDML models were never exposed to these larger clusters during training since the objective was to reproduce the 1-, 2-, and 3-body PES. Thus, training a NequIP on only trimers represents a straightforward comparison to mbGDML. These models were then tested against the identical isomers discussed above, with the results shown in Table . While NequIP can expectedly extrapolate to larger clusters, the Training on large clusters or bulk systems is likely more efficient if a lower scaling method is satisfactory. However, mbGDML becomes particularly useful when applications require force fields based on higher scaling methods. Recovering truncated higherorder contributions would also expectedly improve errors, but explicit 4-body interactions are rather challenging due to high demands on precision and combinatorics. Electrostatic 61 and more general quantum embedding approximations may be a practical route to avoid calculating higher-order contributions, but they are not considered here.