Study of the ion mobility in defect-laden ${\mathrm{ZrO}}_2$ under an electric field using neural network with predictions for Born effective charges

Journal article

Creator

Anh Khoa Augustin Lu (author) (Search by this author)

https://orcid.org/0000-0003-4702-0933

National Institute for Materials Science

NIMS Researchers Directory SAMURAI

Anh Khoa Augustin Lu ;

Naoki Maekawa (author) (Search by this author)

Naoki Maekawa ;

Akane Ikeda (author) (Search by this author)

Akane Ikeda ;

Koji Shimizu (author) (Search by this author)

Koji Shimizu ;

Hiroshi Masuda (author) (Search by this author)

https://orcid.org/0000-0003-1032-8790 (unauthenticated)

National Institute for Materials Science

Hiroshi Masuda ;

Hidehiro Yoshida (author) (Search by this author)

https://orcid.org/0000-0002-0759-8258

National Institute for Materials Science

NIMS Researchers Directory SAMURAI

Hidehiro Yoshida ;

Satoshi Watanabe (author) (Search by this author)

https://orcid.org/0000-0002-8069-6938 (unauthenticated)

National Institute for Materials Science

Satoshi Watanabe

Keyword

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

Date published

2026-06-02

Updated at

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