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Department of Physics, University of Virginia, Charlottesville, VA 22904, USA
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Gauge-equivariant GNNs unlock the ability to learn intrinsically nonlocal observables in lattice gauge theories by directly embedding non-Abelian symmetries into message passing.
Machine learning can now accurately model complex magnetization dynamics in metallic spin systems, even capturing non-equilibrium effects and paving the way for quantum-accurate spintronics simulations.
GNNs offer a surprisingly simple and scalable way to build symmetry-aware force fields for simulating the dynamics of correlated lattice systems, outperforming descriptor-based neural networks.
