Figure shows the true positive rate (TPR) vs. false positive rate (FPR) when using the regression model as a binary classifier to identify whether a given path's E A is less than a chosen threshold. For low thresholds of E A ∼ 1 eV, the model has about a 10 times higher TPR than FPR. We therefore expect the model to differentiate accurately between fast-and slow-diffusing materials, even if it is not able to provide quantitative comparisons among the most exceptional candidates (i.e., those with connected diffusion pathways with E A ≪ 1 eV). Figure show the test set predictions with error bars corresponding to σ pi , which is a useful uncertainty metric on average that is correlated with the model's residual for a given prediction (Figure ). Finally, Figure shows two test set materials with low mean absolute error (MAE) predictions (top row) and two materials with high MAE predictions (bottom row). Detailed investigations on potential sources of error and uncertainty in the d 2 GNN predictions and the underlying NEB calculations themselves are discussed in Supplementary Section 3 and Supplementary Section 4 (e.g., paths with high atomic overlap, end points corresponding to split vacancies, and more).