We observe similar trends in neural scaling for the invariant GNN, SchNet ( .1). That is, not only do the equivariant GNNs achieve better performance for a given data budget, they achieve increasingly greater performance gains given more training data. This is due to the models' equivariance, which is known to produce greater sample efficiency , but it is interesting to note that this trend persists to much larger and more chemically diverse datasets than were previously considered, which typically include only 10 2 -10 3 molecular geometries from a single molecular species.