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Machine learning the order-disorder Jahn-Teller transition in LaMnO3.

Lorenzo Celiberti1,2, Alexander Ehrentraut1, Luca Leoni3

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This study reveals the Jahn-Teller structural phase transition in LaMnO3 is an order-disorder process driven by MnO6 octahedra distortions. Machine-learning molecular dynamics accurately reproduced experimental properties, highlighting anharmonic effects.

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Area of Science:

  • Condensed Matter Physics
  • Materials Science
  • Computational Materials Science

Background:

  • LaMnO3 exhibits a Jahn-Teller structural phase transition around 750 K.
  • Understanding the microscopic mechanisms of such transitions is crucial for materials design.

Purpose of the Study:

  • To investigate the Jahn-Teller structural phase transition in LaMnO3.
  • To elucidate the order-disorder nature of the transition using advanced computational methods.

Main Methods:

  • Molecular dynamics simulations utilizing machine-learning force fields trained on ab initio data.
  • Analysis of site-site correlation functions and velocity autocorrelation functions.

Main Results:

  • The transition is driven by the ordering of Q2 Jahn-Teller distortions of MnO6 octahedra, acting as the order parameter.
  • Dynamical local distortions persist above the transition temperature (TJT).
  • Simulations accurately reproduced experimental structural and phonon properties, revealing anharmonic effects.

Conclusions:

  • The Jahn-Teller transition in LaMnO3 is confirmed as an order-disorder phenomenon.
  • Machine-learning molecular dynamics provides a robust framework for studying phase transitions in correlated materials.
  • This approach distinguishes order-disorder from displacive transitions via vibrational property analysis.