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Description:

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.

Rights:

• Creative Commons BY Attribution 4.0 International
  Creative Commons BY Attribution 4.0 International

Keyword: descriptor importance, subgroup relevance analysis, exhaustive search, hierarchical clustering, binary rare-earth transition metal, Curie temperature

Date published: 2018-11-15

Publisher:

Journal:

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

Funding:

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

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

MDR DOI:

First published URL: https://doi.org/10.7566/JPSJ.87.113801

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Updated at: 2024-01-05 22:12:36 +0900

Published on MDR: 2023-02-10 10:22:25 +0900