GNNs treat chemical systems as graphs, with nodes representing atoms and edges representing interactions between atom pairs. While traditional chemical graph representations usually equate edges with covalent bonds, GNNs assume edges between all atoms within a given cutoff. All potentials discussed in the following are based on the geometric message passing neural network (GemNet) , which shows excellent performances on established benchmark data sets like MD17 and OC20 as well as QM7-x (see Fig. ). GemNet embeds both the atoms and the interatomic edges via high-dimensional vectors. Both kinds of embeddings are then updated in multiple layers using learnable weight matrices and by passing messages between the edges and atoms within a given cutoff distance. GemNet leverages the full geometric information for this: The interatomic distances, the angles between neighboring edges, and the dihedral angles defined via triplets of edges. From the learned embeddings, energy contributions for each atom and layer are obtained, which are subsequently summed up to calculate the total energy of the system. The whole model is continuously differentiable, which allows calculating the forces via