It should be stressed that this notion of robustness is not necessarily correlated with the test MAE, despite the fact that the robust GemNet models also display much lower MAEs. Indeed, the robustness of traditional bio-organic forcefileds with fixed topologies is very high. However, in this case robustness is gained at the expense of model flexibility. The challenge for ML potentials is that they must be robust without sacrificing flexibility. Our tests show that this is not trivial. On a more positive note, we do find that GemNet potentials with sufficiently large training sets are very robust across the phase space of the QM7-x dataset and beyond.