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Updated: May 6, 2026
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Orientational Transition in a Liquid Crystal Triggered by the Thermodynamic Growth of Interfacial Wetting Sheets
Published on: May 15, 2017
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Machine learning the order-disorder Jahn-Teller transition in LaMnO3.
Lorenzo Celiberti1,2, Alexander Ehrentraut1, Luca Leoni3
1Faculty of Physics, University of Vienna, Vienna, Austria.
The Journal of Chemical Physics
|May 4, 2026
Summary
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.
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.
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