Spatially resolved errors and uncertainties for the SrTiO 3 surface are shown in Fig. , where a lighter color corresponds to a larger error or uncertainty. It is clear that mere atomic errors and uncertainties are not correlated, and the uncertainties provide lots of false positives for the expected error. This is in agreement with earlier findings where the aggregated uncertainty of the entire system was used for an active learning procedure, because the atomic uncertainties failed to correlate with the error. In contrast, the local errors and uncertainties correlate very strongly, so that the local uncertainty can be used to reliably identify high-error regions. dataset. In contrast to the individual force component errors and uncertainties in Fig. which feature little correlation, the local errors and uncertainties are correlated. Furthermore, the maximum local uncertainty, Fig. , correlates with the overall mean absolute error averaged over all atoms in the data point, so that the locally aggregated force uncertainties can both be used to select high-error local substructures within a data point, but also high-error data points. Fig. shows the distribution of the ratio of local error versus local uncertainty for different cutoff radii. Again it is seen that the atomic uncertainties are uncorrelated with the corresponding absolute error. In a molecular system like water, one might intuitively aggregate over individual water molecules. Fig. shows that even such a highly localized aggregation, r cut = 1 Å, leads to a certain degree of correlation. For larger cutoff radii, the correlation becomes stronger and the locally aggregated uncertainty and error naturally converge to the globally aggregated quantities eventually. The choice of cutoff radius thus presents a trade-off between the quality of the correlation and the locality of the obtained values.