Equivariant Neural Networks for Force-Field Models of Lattice Systems

TL;DR

This paper introduces a symmetry-preserving equivariant neural network framework for modeling force fields in lattice systems, enabling accurate large-scale simulations that respect the intrinsic symmetries of condensed-matter models.

Contribution

It develops a general, data-driven approach embedding lattice symmetries into neural networks, improving transferability and applicability to various lattice Hamiltonians.

Findings

Successfully modeled adiabatic dynamics of the Holstein Hamiltonian.

Accurately captured mesoscale symmetry-breaking phase evolution.

Demonstrated utility of lattice-equivariant architectures for condensed-matter simulations.

Abstract

Machine-learning (ML) force fields enable large-scale simulations with near-first-principles accuracy at substantially reduced computational cost. Recent work has extended ML force-field approaches to adiabatic dynamical simulations of condensed-matter lattice models with coupled electronic and structural or magnetic degrees of freedom. However, most existing formulations rely on hand-crafted, symmetry-aware descriptors, whose construction is often system-specific and can hinder generality and transferability across different lattice Hamiltonians. Here we introduce a symmetry-preserving framework based on equivariant neural networks (ENNs) that provides a general, data-driven mapping from local configurations of dynamical variables to the associated on-site forces in a lattice Hamiltonian. In contrast to ENN architectures developed for molecular systems -- where continuous Euclidean symmetries dominate -- our approach aims to embed the discrete point-group and internal symmetries intrinsic to lattice models directly into the neural-network representation of the force field. As a proof of principle, we construct an ENN-based force-field model for the adiabatic dynamics of the Holstein Hamiltonian on a square lattice, a canonical system for electron-lattice physics. The resulting ML-enabled large-scale dynamical simulations faithfully capture mesoscale evolution of the symmetry-breaking phase, illustrating the utility of lattice-equivariant architectures for linking microscopic electronic processes to emergent dynamical behavior in condensed-matter lattice systems.