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Important Descriptors and Descriptor Groups of Curie Temperatures of Rare-earth Transition-metal Binary Alloys

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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.

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