The break down in power-law scaling is indicative of "resolution-limited" neural scaling , where the model is sufficiently large but the dataset is not, or vice-versa. Identifying these resolution-limited regimes from the neural scaling relations allows us to understand in general terms whether model performance is limited by data availability or model capacity. The scaling exponent β is equal to 0.17 Graph neural network (GNN) interatomic potentials exhibit robust neural scaling behavior. The potential benefits of large-scale GNNs are less clear than for LLMs, as are the relevant parameters to vary, due to the inequivalence of depth and width for GNNs and additional parameters beyond notions of model size that impact performance, e.g., nearest-neighbor cutoff in graph construction. To simplify GNN scaling experiments, here we vary GNN capacity (depth * width) by systematically changing network width and the number of convolutions (depth). We train GNNs to predict atomic forces from the ANI-1x dataset , the largest publicly available dataset of energies and forces for small molecules. NFF models are trained with a learning rate scheduler that reduces the learning rate every 50 epochs without improvement in the validation loss, until the learning rate reaches 10 -7 . The loss is an L1 loss (Equation ), shown in Figure over four orders of magnitude of dataset size.