Most of the GNN advances have focused on more expressive ways of defining atom (node) representations and local interactions in increasingly large neighbourhoods centred on each of the nodes ('k-hops'). For example, SchNet starts with atom embeddings based on the atom type and atom-wise layers Traditionally, simple functions that satisfy permutation invariance such as sum, mean, or maximum are used for this step. Alternatively, a more expressive molecular representation can be computed by neural networks, for example using attention to discover the most relevant atomic features.