This paper offers a structured and in-depth guide to the field of Geometric Graph Neural Networks (GNNs) for 3D atomic systems, introducing a taxonomy of invariant, equivariant (Cartesian and spherical), and unconstrained models to help newcomers and practitioners better understand and navigate the landscape of geometric GNN architectures, applications, and future directions.
Authors: Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein
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