Explainable machine learning for materials discovery: predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship.

Pham, Tien-Lam; Nguyen, Duong-Nguyen; Ha, Minh-Quyet; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi

Pham, Tien-Lam; Nguyen, Duong-Nguyen; Ha, Minh-Quyet; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi.

Afiliação

Pham TL; Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
Nguyen DN; ESICMM, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
Ha MQ; Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
Kino H; Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
Miyake T; ESICMM, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
Dam HC; Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.

IUCrJ ; 7(Pt 6): 1036-1047, 2020 Nov 01.

Article em En | MEDLINE | ID: mdl-33209317

Palavras-chave

data mining; first-principles calculations; machine learning; materials informatics; new magnets

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: IUCrJ Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Japão

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google