Journal
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
Volume 87, Issue 11, Pages -Publisher
PHYSICAL SOC JAPAN
DOI: 10.7566/JPSJ.87.113801
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Funding
- PRESTO from the Japan Science and Technology Agency (JST), Japan
- Materials Research by Information Integration Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub from the Japan Science and Technology Agency (JST), Japan
- Elements Strategy Initiative Project under the MEXT
- MEXT as a social and scientific priority issue (Creation of New Functional Devices and High-Performance Materials to Support Next-Generation Industries
- CDMSI)
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We analyze the Curie temperatures of rare-earth transition metal binary alloys using machine learning. In order to select important descriptors and descriptor groups, we introduce a newly developed subgroup relevance analysis and adopt hierarchical clustering in the representation. We execute exhaustive search and demonstrate that our approach results in the successful selection of important descriptors and descriptor groups. It helps us to choose the combination of descriptors and to understand the meaning of the selected combination of descriptors.
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