Besides the obvious intrinsic benefit of reliably quantifying the uncertainty of an ML model, these estimates are also a vital part of the so-called active-learning (AL) algorithms. AL denotes a strategy where the model constructs new (training) data points either in regions where a property of interest needs to be optimized (exploitation task) or in regions where the model uncertainty is large (exploration task), resulting in a more accurate model with a lower amount of training points. In material science, these algorithms are often desirable, because little initial information is known about a material or materials class and calculating labels (properties) is expensive.