5 papers
Graph-Recurrent Learning of Emergent Collective Dynamics
Ali Rayat, Acacia Lee, Gia-Wei Chern
Collective ordering in correlated electron systems emerges from microscopic dynamics, yet the evolution of observable textures need not admit a closed, memoryless description. We i…
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks
Ali Rayat, Gia-Wei Chern
Gauge-equivariant graph neural networks generate ordered products of gauge links through local message passing, but the geometry of these internal structures is not captured by the…
Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
Ali Rayat, Yunhao Fan, Gia-Wei Chern
Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically require repeated solutions of an…
Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
Ali Rayat, Gia-Wei Chern
Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective descriptions of strongly corr…
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Ali Rayat, Yaohang Li, Gia-Wei Chern
Local gauge symmetry underlies fundamental interactions and strongly correlated quantum matter, yet existing machine-learning approaches lack a general, principled framework for le…