Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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UNVERDICTED 6representative citing papers
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
A machine learning Hamiltonian model trained on small 95-atom supercells enables linear-scaling structural relaxations and accurate formation energy predictions for oxygen vacancies in larger supercells of amorphous SiO2, with deviations below 50 meV from DFT due to error cancellation.
MD simulations with a machine-learned potential demonstrate that structural fracture energy in silica glass increases with crack velocity below the branching threshold due to both elevated intrinsic surface energy density and nanoscale roughening.
AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.
citing papers explorer
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Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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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 Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO$_2$
A machine learning Hamiltonian model trained on small 95-atom supercells enables linear-scaling structural relaxations and accurate formation energy predictions for oxygen vacancies in larger supercells of amorphous SiO2, with deviations below 50 meV from DFT due to error cancellation.
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Energy dissipation at the atomic scale explains how fracture energy depends on crack velocity in silica glass
MD simulations with a machine-learned potential demonstrate that structural fracture energy in silica glass increases with crack velocity below the branching threshold due to both elevated intrinsic surface energy density and nanoscale roughening.
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AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials
AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.
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Differentiable hybrid force fields support scalable autonomous electrolyte discovery
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.