For the first aspect, in the realm of surrogate model potentials, Bayesian-based frameworks offer an intrinsic definition of uncertainty, which can be judiciously used . For neural-network architectures, committee ensemble models can deliver some degree of uncertainty prediction. In both cases, correctly accounting for correlations in the training set data is essential for avoiding overconfident model predictions , but UQ can still be unreliable for out-of-sample data points. A promising alternative is the use of deep ensembles or variations thereof. Finally, because the surrogate model is trained to predict energy and forces, but these quantities are almost never the observable that is being sought in a simulation, advances in error definition and propagation through derived properties have been gaining much attention .