A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

arXiv:2312.07511v2 Announce Type: replace
Abstract: Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations. In recent years, Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from …

abstract advances arxiv computational cs.ai cs.lg embedded gnns graphs guide materials modelling molecules nodes proteins q-bio.qm space stat.ml systems them type