Graph Neural Networks (GNNs). We train GNNs to predict the forces of molecular geometries. Force-only training (α E = 0 in Eq. 5) was used for neural scaling experiments to improve convergence and avoid issues with systematic drift in predicted energies, which we identified during the course of this work and plan to address in future work. We use the SchNet , PaiNN , Allegro , and SpookyNet models. Model implementations are from the NeuralForceField repository and the Allegro repository . Model sizes (w in Equation ) were varied between 16, 64, and 256, while the number of layers/convolutions (d in Equation ) was chosen to be 2, 3, or 4. A 5 Å nearest-neighbor cutoff was used. All other model hyperparameters were set to default values from the original implementations. GNN models were trained with SGD using the Adam optimizer.