One of the most recent additions to this family of methods are potentials based on graph neural networks (GNNs), such as SchNet, DimeNet, GemNet and NequIP. Here, much progress towards ever more accurate and expressive potentials has been made, e.g. by using equivariant formulations or embedding atom pairs and triplets. While such efforts naturally focus on established benchmark databases like QM9 , MD17 or OC20 , comparatively little research has been conducted to show the applicability of such advanced GNN potentials in real atomistic simulations. A notable exception to this is a recent paper of Batzner and coworkers , which demonstrated that potentials based on the equivariant NequIP architecture could be used in stable and accurate MD simulations, when trained on AIMD data for the respective system.