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Study of the ion mobility in defect-laden ZrO2 under an electric field using neural network with predictions for Born effective charges

Anh Khoa Augustin Lu1,2,*, Naoki Maekawa1, Akane Ikeda1, Koji Shimizu3, Hiroshi Masuda1, Hidehiro Yoshida1, and Satoshi Watanabe1,†

  • *Contact author: LU.Augustin@nims.go.jp
  • †Contact author: watanabe@cello.t.u-tokyo.ac.jp
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Phys. Rev. Materials 10, 066001 – Published 2 June, 2026

DOI: https://doi.org/10.1103/jcsd-dbl2

Abstract

Unusual mass transport behavior in tetragonal ZrO2 ceramics has attracted attention under flash events induced by strong electric fields. However, this observation cannot be attributed solely to Joule heating, suggesting the importance of understanding the ion behaviors associated with defective states under a strong electric field. Previous studies have studied the impact of an external electric field but were typically limited to fixed formal charges for the ions. In this work, to incorporate the response of ions to an electric field, we calculate Born effective charges, and use them in addition to the energy and forces to train neural network potentials. Our molecular dynamics simulations using trained models show that under an applied electric field, the diffusivity of oxygen ions is enhanced in defect-laden ZrO2 with a preexisting oxygen vacancy, which could be associated with the observed unusual mass transport behavior. This is a milestone towards the accurate description of defect-laden materials under an applied electric field.

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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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Outline

Information

Phys. Rev. Materials 10, 066001– Published 2 June, 2026
  • Received 18 August 2025
  • Revised 12 March 2026
  • Accepted 4 May 2026
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