A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
Behler, Perspective: Machine learning potentials for atomistic simulations, The Journal of Chemical Physics 145, 170901 (2016)
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Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.
At higher concentrations lithium electrolytes shift from solvent-dominated coordination to ion pairing and correlated domains, making screening and transport emerge from the same underlying structures rather than independent processes.
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Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.
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A molecular perspective on coordination, screening, and emergent length scales in lithium electrolytes
At higher concentrations lithium electrolytes shift from solvent-dominated coordination to ion pairing and correlated domains, making screening and transport emerge from the same underlying structures rather than independent processes.