In the following we will use Monte Carlo simulation to corroborate that e i and s i are strongly correlated whereas e k ij and s k ij are not. For a dataset of N i structures consisting of N j atoms each, we draw σ k ij from an inverse-Gamma distribution as proposed in Ref. 28, so that each combination of i, j, and k gets assigned its own groundtruth uncertainty. We then draw from normal distributions with µ = 0 and σ = σ k ij to obtain N C model predictions of a committee for each combination of i, j, and k. Subsequently, we compute the committee standard deviation s k ij and absolute error e k ij as well as the averaged values s i and e i within a data point, Eqs. ( ) and ( ), from the committee predictions. The simulation thus corresponds to a simple artificial system, where an otherwise perfect model only features variance error. Fig. depicts histograms of the error-uncertainty ratio for the individual and aggregated case, as well as scatter plots of the errors versus uncertainties. In the Supporting Information, we furthermore report error-uncertainty ratios as a function of the size of the committee. For N C = 10, the conversion factor between error and uncertainty from Eq. ( ) is 2/ √ 2πN C = 0.25. Clearly, both the individual and aggregated versions converge to a ratio of 0.25 averaged over all data points (gray dashed lines). However, as expected the individual error-uncertainty ratios are broadly distributed so that there is no linear dependency between error and uncertainty for the individual data points, as visible in Fig. . Fig. and Fig. furthermore depict that the distribution of the predicted values around the target lead to more predictions being observed towards the center of the distribution (small error, dark purple) than its outskirts (orange). Therefore, many individual uncertainty values obtained from the committee standard deviation significantly underestimate the actual model error, so that the individual committee uncertainties cannot be used to identify high-error atoms. The relation between atomic uncertainties and errors is therefore asymmetric, with large errors occurring only for large uncertainties, and small uncertainties permitting only small errors, but not the other way around. In contrast, the ratio of averaged absolute errors over the uncertainties show a narrow distribution as visible in Fig. and Fig. , so that we can actually estimate the prediction error from the uncertainty by multiplication with 0.25, corroborating Eq. ( ). Note that the conversion factor for this artificial system being lower than 1.0 results from the model being unbiased, which is usually not the case for real atomic systems. Monte Carlo simulations were performed for different N C , N i , N j , as well as different distributions for σ k ij , namely uniformly random distributions within an interval, normal distributions, and even constant values, and observe the same behavior across all systems: The individual ratios are centered around 2/ √ 2πN C but are distributed too broadly to show a meaningful correlation between the absolute errors and uncertainties, while the ratio of the respective aggregated values are distributed in a narrow peak yielding a strong correlation.