Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations

TL;DR

This paper introduces a machine learning-based large-scale simulation method to study charge density wave coarsening in correlated electron systems, revealing how electron interactions enhance coarsening dynamics.

Contribution

The study develops a linear-scaling ML algorithm for simulating CDW coarsening in correlated electrons, providing new insights into electron correlation effects on non-equilibrium dynamics.

Findings

Electron correlations enhance charge density wave coarsening.

ML approach enables efficient large-scale simulations.

Screening effects influence the coarsening process.

Abstract

The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard theories. However, accurate treatment of both quasiparticles and collective degrees of freedom is a multi-scale challenge in dynamical simulations of correlated electrons. Here we leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs), one of the fundamental symmetry breaking phases in functional electron materials. We demonstrate our approach on the square-lattice Hubbard-Holstein model and uncover an intriguing enhancement of CDW coarsening which is related to the screening of on-site potential by electron-electron interactions. Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches for advancing multi-scale dynamical modeling of correlated electron systems.