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

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.