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

Lorenzo Celiberti; Alexander Ehrentraut; Luca Leoni; Cesare Franchini

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arXiv · 2604.08058

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

Lorenzo Celiberti·Alexander Ehrentraut·Luca Leoni·Cesare Franchini

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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.

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Lorenzo Celiberti, Alexander Ehrentraut, Luca Leoni, Cesare Franchini. 2026-04-09. Machine Learning the order-disorder Jahn-Teller transition in LaMnO$_3$. https://arxiv.org/abs/2604.08058

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