Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations

TL;DR

This paper introduces a machine learning force-field model for simulating the orbital ordering dynamics in cooperative Jahn-Teller systems, enabling large-scale, efficient simulations that reveal complex coarsening behaviors in manganites.

Contribution

A deep-learning neural-network model incorporating symmetry principles is developed for large-scale simulations of Jahn-Teller dynamics in correlated electron materials.

Findings

Large-scale simulations reveal orbital domain coarsening and freezing behaviors.

The ML model accurately predicts forces, enabling efficient dynamical studies.

Insights into the structural transition and orbital order emergence in manganites.

Abstract

We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems. Large scale dynamical simulations of the JT model also shed light on the orbital ordering dynamics in colossal magnetoresistance manganites. The JT effect in these materials describes the distortion of local oxygen octahedra driven by a coupling to the orbital degrees of freedom of $e_g$ electrons. An effective electron-mediated interaction between the local JT modes leads to a structural transition and the emergence of long-range orbital order at low temperatures. Assuming the principle of locality, a deep-learning neural-network model is developed to accurately and efficiently predict the electron-induced forces that drive the dynamical evolution of JT phonons. A group-theoretical method is utilized to develop a descriptor that incorporates the combined orbital and lattice symmetry into the ML model. Large-scale Langevin dynamics simulations, enabled by the ML force-field models, are performed to investigate the coarsening dynamics of the composite JT distortion and orbital order after a thermal quench. The late-stage coarsening of orbital domains exhibits pronounced freezing behaviors which are likely related to the unusual morphology of the domain structures. Our work highlights a promising avenue for multi-scale dynamical modeling of correlated electron systems.