Connectivity Optimized Nested Graph Networks for Crystal Structures. (arXiv:2302.14102v2 [cs.LG] UPDATED)

Graph neural networks (GNNs) have been applied to a large variety of
applications in materials science and chemistry. Here, we recapitulate the
graph construction for crystalline (periodic) materials and investigate its
impact on the GNNs model performance. We suggest the asymmetric unit cell as a
representation to reduce the number of atoms by using all symmetries of the
system. This substantially reduced the computational cost and thus time needed
to train large graph neural networks without any loss in accuracy. …

applications arxiv chemistry connectivity construction gnns graph graph neural networks impact materials materials science networks neural networks performance reduce representation science