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説明:

We analyze Curie temperatures of rare-earth transition metal binary alloys with machine
learning method. In order to select important descriptors and descriptor groups, we intro-
duce newly developed subgroup relevance analysis and adopt the hierarchical clustering in
the representation. We execute the exhaustive search and illustrate that our approach indeed
leads to the successful selection of important descriptors and descriptor groups. It helps us
to choose the combination of the descriptors and to understand the meaning of the selected
combination of descriptors.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

  Creative Commons BY Attribution 4.0 International

キーワード: descriptor importance, subgroup relevance analysis, exhaustive search, hierarchical clustering, binary rare-earth transition metal, Curie temperature

刊行年月日: 2018-11-15

出版者:

掲載誌:

• JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (ISSN: 00319015) vol. 87 issue. 11 p. 113801-113801

研究助成金:

• JST Materials Research by Information Integration” Initiative (MI2I) project
• MEXT CDMSI

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.7566/JPSJ.87.113801

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更新時刻: 2024-01-05 22:12:36 +0900

MDRでの公開時刻: 2023-02-10 10:22:25 +0900