Fig. and Fig. furthermore depict heatmaps of the individual and molecular errors versus uncertainties for the Transition1x dataset, since the actual functional dependence is hard to read from Fig. due to the large number of test data points. Here, it becomes obvious that even for this difficult extrapolation task, the aggregation successfully leads to a highly correlated molecular error and uncertainty. The Transition1x dataset is especially interesting to research the correlation between error and uncertainty, since we can resolve it with respect to the NEB image index. Since indices 1, 2, 9, and 10 were used in the training and validation sets, we can plot the test set performance and uncertainty met- rics over the indices 3, 4, 5, 6, 7, and 8. The indices 5 and 6 are the most dissimilar to the training set, which has never seen any transition state structures, but only (close-to) equilibrium structures. We therefore expect the model to have a much larger model bias for the indices 5 and 6, than the indices 3 and 8. Fig. depicts the mean absolute errors and mean uncertainty over all data points as a function of the index. We find that the model performs worst for indices 5 and 6, as expected. The model furthermore identifies these predictions as the most uncertain, featuring a high aggregated committee standard deviation. Further, the model is able to identify a higher model bias (extrapolation error), Fig. , where the distribution of α shifts to higher values, the more the model encounters data points far away from the training set configurations. The values change from 0.93 for index 8 to 1.21 for index 5, as opposed to a pure-variance model with α = 2/ √ 2πN C = 0.36 (all values given in the Supporting Information). Since Eq. ( ) directly outputs the expected amount of variance error for a given dataset and model architecture, we can furthermore compute the fraction of variance and non-variance error of the overall error. The variance error is shaded in dark gray in Fig. , as well as the non-variance error in light gray, which both visibly increase for structures far away from equilibrium. Together, the overall uncertainty strongly correlates with the actual error. The good correlation of aggregated uncertainties with aggregated errors for models with a significant amount of bias error may seem surprising, since technically the relation in Eq. ( ) relation only holds for errors stemming from variance. Recently, the bias error was found to be correlated with the variance error when changing the dataset size or model architecture. Here, we also find that the presence of model bias only changes the value of α and broadens its distribution slightly, but preserves the correlation. In fact, we find that the bias and variance error are correlated for machine learning potentials (the variance error also increased for index 5 and 6), but the amount of bias error differs between the test sets with different degrees of extrapolation. To further illustrate the behavior of α, we also trained less accurate versions of the presented MACE models and models based on the NeuralIL architecture. The results are compiled in the Supplementary Information, together with correlation coefficients, mean α and model bias percentages for all model types. Overall, the aggregated force uncertainties reliably identify sets of data points with a higher model bias, here extrapolation error due to missing training data, which is an important prerequisite for successful active learning cycles.