Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations

TL;DR

This paper uses machine learning to enable large-scale kinetic Monte Carlo simulations, revealing complex phase separation dynamics in a correlated electron system, including multi-scale domain coarsening and symmetry breaking.

Contribution

It introduces a novel machine learning approach for large-scale simulations of phase separation in correlated electron systems, uncovering multi-scale coarsening and hidden symmetry breaking.

Findings

Unusual multi-scale domain coarsening observed.

Emergence of super-clusters due to symmetry breaking.

Arrested growth caused by correlation-induced self-trapping.

Abstract

Phase separation plays a central role in the emergence of novel functionalities of correlated electron materials. The structure of the mixed-phase states depends strongly on the nonequilibrium phase-separation dynamics, which has so far yet to be systematically investigated, especially on the theoretical side. With the aid of modern machine learning methods, we demonstrate the first-ever large-scale kinetic Monte Carlo simulations of the phase separation process for the Falicov-Kimball model, which is one of the canonical strongly correlated electron systems. We uncover an unusual phase-separation scenario where domain coarsening occurs simultaneously at two different scales: the growth of checkerboard clusters at smaller length scales and the expansion of super-clusters, which are aggregates of the checkerboard clusters of the same sign, at a larger scale. We show that the emergence of super-clusters is due to a hidden dynamical breaking of the sublattice symmetry. Arrested growth of the checkerboard patterns and of the super-clusters is shown to result from a correlation-induced self-trapping mechanism. Glassy behaviors similar to the one reported in this work could be generic for other correlated electron systems.