Some fluff in this PR piece, but I love the message that molecular chemistry can now also benefit from deep learning advances, thanks to graph neural networks. Because molecules are by their nature graphs of atoms, it makes sense to learn a latent representation of a molecular entity with a GNN, and use that for various classification or prediction tasks.

Makes me wonder if the computationally expensive geometry optimization calculations using DFT etc. can be partially done by a GNN. So, pre-optimize a structure with a computationally cheaper GNN to speed up convergence.