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Finite-temperature ferroelectric phase transitions from machine-learned force fields

Kristoffer Eggestad, Ida C. Skogvoll, Øystein Gullbrekken, Benjamin A. D. Williamson, and Sverre M. Selbach*

  • *Contact author: selbach@ntnu.no
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Phys. Rev. Materials 10, 034409 – Published 16 March, 2026

DOI: https://doi.org/10.1103/nkc5-rtds

Abstract

Simulating finite-temperature phase transitions from first principles is computationally challenging. Recently, molecular-dynamics (MD) simulations using machine-learned force fields (MLFFs) have opened a new avenue for finite-temperature calculations with near-first-principles accuracy. Here we use MLFFs, generated using on-the-fly training, to investigate structural phase transitions in four of the most well-studied ferroelectric oxides: BaTiO3, PbTiO3, LiNbO3, and BiFeO3. Only using the 0 K ground-state structure as input for the training, the resulting MLFFs can qualitatively predict all the main structural phases and phase transitions, while the quantitative results are sensitive to the choice of exchange-correlation functional with PBEsol found to be more robust than LDA and r2SCAN. MD simulations also reproduce the experimentally observed order-disorder character of Ti displacements in BaTiO3, the abrupt first-order transitions of BiFeO3 and PbTiO3, and the mixed order-disorder and displacive character of the ferroelectric transition in LiNbO3. Finally, we discuss the potential and limitations of using MLFFs for simulating ferroelectric phase transitions.

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Machine Learning for Materials Discovery and Understanding

The Editors of Physical Review Materials are pleased to present the Collection on Machine Learning for Materials Discovery and Understanding, highlighting cutting-edge advances in machine learning method development and applications for materials discovery and fundamental understanding of the structure-property-function relationship. The Collection is being guest-edited by Deyu Lu of Brookhaven National Laboratory (USA) and Jinlan Wang of Southeast University (China). Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review Materials editorial team managed the peer review and made all editorial decisions.

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Phys. Rev. Materials 10, 034409– Published 16 March, 2026
  • Received 17 November 2025
  • Accepted 29 January 2026
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