Machine Learning the order-disorder Jahn-Teller transition in LaMnO
arXiv:2604.08058
Abstract
We investigate the Jahn-Teller structural phase transition in LaMnO at 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 Jahn-Teller distortion of the MnO octahedra, which acts as the order parameter and establishes the order-disorder nature of the transition. Dynamical local distortions are found to persist above . 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.
Accepted for publication in JCP