Machine Learning the order-disorder Jahn-Teller transition in LaMnO$_3$

TL;DR

This study uses machine-learning molecular dynamics to analyze the Jahn-Teller transition in LaMnO₃, revealing the disorder-order nature driven by specific distortions and capturing temperature-dependent properties.

Contribution

It introduces a machine-learning based framework to investigate the microscopic mechanisms of structural phase transitions, distinguishing order-disorder from displacive mechanisms.

Findings

Transition driven by ordering of Q₂ Jahn-Teller distortion

Dynamical distortions persist above transition temperature

Reproduces experimental structural and phonon temperature dependence

Abstract

We investigate the Jahn-Teller structural phase transition in LaMnO$_3$ at $T_{JT} \simeq 750$ K using molecular dynamics simulations based on machine-learning force fields trained on ab initio data. Analysis of the site-site correlation function of the distortions reveals that the transition is driven by the ordering of the $Q_2$ Jahn-Teller distortion of the MnO$_6$ octahedra, which acts as the order parameter and establishes the order-disorder nature of the transition. Dynamical local distortions are found to persist above $T_{JT}$. Our results reproduce the experimental temperature dependence of both structural and phonon properties and highlight the presence of anharmonic effects at finite temperature. More broadly, the combined use of machine-learning molecular dynamics and velocity autocorrelation function analysis provides a robust framework for uncovering the microscopic mechanisms of structural phase transitions in correlated materials. In particular, this approach enables a clear distinction between order-disorder transitions and alternative mechanisms, such as displacive behavior, through the temperature evolution of vibrational properties.