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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A composition-weighted symbolic regression framework learns analytical expressions and elemental weightings from composition to predict materials properties with accuracy competitive to black-box models while producing explicit, constraint-enforcing formulas.
pDOS-augmented GNN node features reduce Tc and ε∞ prediction errors by 22.9% and 27.9% versus elemental baselines, matching roughly a 1.7–1.8× increase in training data.
citing papers explorer
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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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Composition-Weighted Symbolic Regression for General-Purpose Property Prediction
A composition-weighted symbolic regression framework learns analytical expressions and elemental weightings from composition to predict materials properties with accuracy competitive to black-box models while producing explicit, constraint-enforcing formulas.
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Machine Learning Materials Properties by Encoding Orbital-Projected Density of States
pDOS-augmented GNN node features reduce Tc and ε∞ prediction errors by 22.9% and 27.9% versus elemental baselines, matching roughly a 1.7–1.8× increase in training data.