The marked discrepancy between the test set and MD performance of the 3.2k model underscores the limitations of using test configurations that are not generated by the potential itself. For a ML model to be useful in atomistic simulations, it is not sufficient to show that it provides accurate fits for physically reasonable configurations. It is equally important that the model avoids unphysical configurations in its own simulations. Note that testing this requires sufficiently long trajectories. This is illustrated for a representative example in Fig. . Here, the error of the 3.2k model is actually quite low for the first 700 ps of the simulation after which it rises sharply to more than 20 eV due to an unphysical bond dissociation event. This behaviour can be understood as a kind of 'hole' in the potential energy surface of the model. This hole can be rather small, but once the simulation reaches such a configuration the trajectory is completely unreasonable. The 'robustness' of a ML potential can thus be understood as a measure of how frequent and how large such holes in the potential energy surface are. Ultimately this can only be quantified by performing MD simulations with the corresponding potential.