In the Supporting Information, we report detailed uncertainty metrics comparing atomic, local, and perstructure errors and uncertainties, and find excellent correlation between the spatially resolved local errors and uncertainties developed in this study. The local uncertainties can be directly used to estimate local errors for the Transition1x, SrTiO 3 , and water data sets, and feature high correlation coefficients, Spearman's rank coefficients, as well as excellent error-based calibration. We furthermore note that the local uncertainties do not require a recalibration, but only a simple fit of α to obtain direct error estimates via αs local ij . In addition, the strong correlation between local error and uncertainty makes the obtained uncertainties sharp, tight, and disperse. Sharpness quantifies the mean prediction uncertainty, where a method yielding high uncertainties across all predictions (low sharpness) is undesirable. Tightness refers to the small-scale reliability of uncertainty predictions with respect to reference values, and is high for a perfect calibration. Dispersion describes the spread of observed uncertainties, where a model predicting the same uncertainty across all predictions (low dispersion) is undesirable. For all data sets and models reported, we observe sharp, tight, and disperse local uncertainty predictions due to their direct, strong correlation with error.