ジャーナル論文 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) (この著者で検索)
ORCID SAMURAI ;
Naoki Maekawa (author) (この著者で検索)
;
Akane Ikeda (author) (この著者で検索)
;
Koji Shimizu (author) (この著者で検索)
;
Hiroshi Masuda (author) (この著者で検索)
ORCID https://orcid.org/0000-0003-1032-8790 (unauthenticated)
National Institute for Materials Science
ORCID ;
Hidehiro Yoshida (author) (この著者で検索)
ORCID SAMURAI ;
Satoshi Watanabe (author) (この著者で検索)
ORCID https://orcid.org/0000-0002-8069-6938 (unauthenticated)
National Institute for Materials Science
ORCID
コレクション

引用
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

説明:

(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.

権利情報:

キーワード: Machine learning force field, Zirconia, Ion diffusion, Point defect, Electric field

刊行年月日: 2026-06-02

出版者: American Physical Society (APS)

掲載誌:

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

研究助成金:

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

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1103/jcsd-dbl2

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その他の識別子:

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更新時刻: 2026-06-08 13:01:57 +0900

MDRでの公開時刻: 2026-06-08 15:26:37 +0900

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