For all datasets, we observe large areas in the sparsification plots for the individual force uncertainties showing that they do not correlate with the absolute error, and cannot be used to order the test set according to the estimated error. In fact, as we have explored in the previous subsection, the single individual force uncertainties and errors cannot correlate for mathematical reasons. To obtain a direct correlation, uncertainties and errors have to be averaged over sets of atoms, here the full molecule or structure, to make use of Eq. ( ). Although the data sets have a different number of data points, and a different number of atoms per data point, they all show a large improvement in correlation between error and uncertainty upon aggregation over all atoms in a data point. In all cases, an ordering of the predicted values according to their uncertainty corresponds nearly perfectly to the oracle ordering according to the true prediction error, and the distribution of α is narrow. Note that the distribution of α is not centered around the expected value of 2/ √ 2πN C anymore, since all models feature differing amounts of model bias, so that the error from model variance is only a small part of the overall prediction error. Moreover, the model error may not be Gaussian anymore, causing further deviations from Eq. ( ). However, this only affects the magnitude of α (since a change in distribution changes its first moment), but does not impede the quality of correlation between the aggregated errors and uncertainties, as long as the error is still symmetric around zero, and the model still performs adequately for the data of interest (see Supporting Information for cases with low model performance).