Preprint
Oct 2026
Unconstrained learning of equivariant atomistic properties
Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, often O(3)-equivariant by design, are increasingly used to learn other atomistic properties. Many of these are of high angular order or pseudo...
Joseph W. Abbott, Paolo Pegolo, Pol Febrer et al.
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