Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians

• URL: http://arxiv.org/abs/2603.02039v1
• Date: Mon, 02 Mar 2026 16:23:25 GMT
• Title: Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
• Authors: Yunhao Fan, Gia-Wei Chern,
• Abstract summary: We develop a graph neural network (GNN)-based force-field framework for the adiabatic dynamics of lattice Hamiltonians.<n>Trained on exact-diagonalization data, the GNN achieves high force accuracy, strict linear scaling with system size, and directability to large lattices.<n>These results establish GNNs as an elegant and efficient architecture for symmetry-aware, large-scale dynamical simulations of correlated lattice systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scalable and symmetry-consistent force-field models are essential for extending quantum-accurate simulations to large spatiotemporal scales. While descriptor-based neural networks can incorporate lattice symmetries through carefully engineered features, we show that graph neural networks (GNNs) provide a conceptually simpler and more unified alternative in which discrete lattice translation and point-group symmetries are enforced directly through local message passing and weight sharing. We develop a GNN-based force-field framework for the adiabatic dynamics of lattice Hamiltonians and demonstrate it for the semiclassical Holstein model. Trained on exact-diagonalization data, the GNN achieves high force accuracy, strict linear scaling with system size, and direct transferability to large lattices. Enabled by this scalability, we perform large-scale Langevin simulations of charge-density-wave ordering following thermal quenches, revealing dynamical scaling and anomalously slow sub--Allen--Cahn coarsening. These results establish GNNs as an elegant and efficient architecture for symmetry-aware, large-scale dynamical simulations of correlated lattice systems.

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