We furthermore note that the generality of our approach allows for application to a variety of systems, and have already utilized spatially resolved uncertainties as developed in this study for surface reconstructions, interfaces and defected structures (results not yet published). These type of configurations consist of substructures featuring higher prediction errors than the rest of the structure, which is well resolved by the demonstrated approach. We therefore envision the current study to spark new applications within machine learning potentials, especially active learning cycles, across a large variety of systems.