Size transferability to hundreds of molecules is paramount for useful ML potentials. Most ML potentials rely on local descrip-tors or graph neural networks (GNNs) that partition total properties into atomic contributions. Local descriptors have been successful in numerous applications, but they inherently neglect or limit complicated non-local interactions by enforcing atomic radial cutoffs. For example, a recent study showed that a deep neural network potential's predictions of liquid water properties are sensitive to training data relevant to the thermodynamic state point. Global descriptors (such as the Coulomb matrix and pairwise atomic distances) impose no such constraints and capture interactions at all scales. Still, they are usually restricted to the same number of atoms.