The neural scaling results for the equivariant GNN, PaiNN (Figure ), show monotonic improvements to the loss with increasing dataset size. For a fixed dataset size, the converged loss is strongly correlated with the total training time (compute) and model capacity. Other than for 10 4 datapoints (for which some small models reach convergence quickly), the converged loss has a Spearman correlation coefficient ρ >= 0.88 with the model capacity and ρ >= 0.75 with the total training time. This means that the best models are those with optimal capacity that are able to train the longest without the validation loss plateauing. The optimal capacity and depth versus width change with the dataset size, i.e., the ideal GNN capacity is dataset-size dependent, and these choices can significantly impact the converged loss. These effects may also be artifacts of random initialization that would diminish with repeated trials. Interestingly, there is a stark change at 10 4 datapoints -the converged loss is then nearly perfectly rank correlated with model capacity (Spearman's ρ >= 0.93). This might indicate that substantial overlap exists between the training and validation set, such that higher capacity models are merely exhibiting better memorization than lower capacity models. In these experiments, the validation set is constructed from unseen geometries and seen species (chemical species are the same in the training and validation sets). Repeating these experiments with a hold-out set of unseen chemical species will reveal if the same trend holds, which would indicate that rather than memorizing, the network is achieving generalization to new chemistries.