Journal article Study of the ion mobility in defect-laden ZrO 2 under an electric field using neural network with predictions for Born effective charges
Anh Khoa Augustin Lu (author) (Search by this author)
ORCID SAMURAI ;
Naoki Maekawa (author) (Search by this author)
;
Akane Ikeda (author) (Search by this author)
;
Koji Shimizu (author) (Search by this author)
;
Hiroshi Masuda (author) (Search by this author)
ORCID https://orcid.org/0000-0003-1032-8790 (unauthenticated)
National Institute for Materials Science
ORCID ;
Hidehiro Yoshida (author) (Search by this author)
ORCID SAMURAI ;
Satoshi Watanabe (author) (Search by this author)
ORCID https://orcid.org/0000-0002-8069-6938 (unauthenticated)
National Institute for Materials Science
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Citation
Anh Khoa Augustin Lu, Naoki Maekawa, Akane Ikeda, Koji Shimizu, Hiroshi Masuda, Hidehiro Yoshida, Satoshi Watanabe. Study of the ion mobility in defect-laden ZrO 2 under an electric field using neural network with predictions for Born effective charges. Physical Review Materials. 2026, 10 (6), 066001. https://doi.org/10.1103/jcsd-dbl2

Description:

(abstract)

Unusual mass transport behavior in tetragonal ZrO2 ceramics have 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 pre-existing oxygen vacancy, which could be associated with the observed unusual mass transport behavior. This is a milestone towards accurate description of defect-laden materials under an applied electric field.

Rights:

Keyword: Machine learning force field, Zirconia, Ion diffusion, Point defect, Electric field

Date published: 2026-06-02

Publisher: American Physical Society (APS)

Journal:

  • Physical Review Materials (ISSN: 24759953) vol. 10 issue. 6 066001

Funding:

  • Japan Society for the Promotion of Science 24K01284
  • Japan Science and Technology Agency JPMJCR1996
  • University of Tokyo

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1103/jcsd-dbl2

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Updated at: 2026-06-08 13:01:57 +0900

Published on MDR: 2026-06-08 15:26:37 +0900

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