Ferroelectric phase transition in group-IV monochalcogenides from an equivariant machine learned force field
Phys. Rev. Materials 9, 103801 – Published 10 October, 2025
DOI: https://doi.org/10.1103/t63g-4zp3
Abstract
Group-IV monochalcogenides are a class of layered ferroelectric semiconductors that have demonstrated spontaneous intrinsic polarization above room temperature. Here, we use the multiatomic cluster expansion machine learning architecture to train and test a force field capable of modeling the structural properties and second-order ferroelectric-to-paraelectric phase transition in a group-IV monochalcogenide, GeSe. The model captures the double-well potential energy surface associated with the onset of macroscopic polarization in bulk GeSe within , as well as near-equilibrium properties like the phonon dispersion. The development of this quantitatively accurate force field enables long-time molecular dynamics simulations, which predict the critical temperature of the ferroelectric-to-paraelectric phase transition in bulk GeSe to be . This study demonstrates the capabilities of equivariant force fields to accurately describe phenomena associated with structural symmetry breaking.
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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.
