Abstract
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 introduce newly developed subgroup relevance analysis and adopt the hierarchical clustering in the representation. We execute the exhaustive search and successfully illustrate the importance of descriptors and descriptor groups. 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.
Highlights
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
Summary
Hieu Chi Dam1,2,3 , Viet Cuong Nguyen[4 ], Tien Lam Pham1,5 , Anh Tuan Nguyen[6 ], Kiyoyuki Terakura1,2 , and Takashi Miyake2,5,7 , and Hiori Kino[2,5]. ( We explain the scores and relevance analysis in the supporting information.18) ) It originally utilizes the change in values when we remove/add a descriptor. The ordering of the scores of the models (combinations of descriptors) can be changed according to the details of the regression scheme and noise in the data, because the differences in the scores are quite small (Table II in the main body and Table I in the supporting information).18) just showing the best models with n descriptors may give us wrong information. We found three major errors and a minor error After fixing these errors, we evaluated the cross-validation test scores again for the best n descriptors of the original regression model. We developed subgroup relevance analysis and successfully illustrated the importance, relationship, and structures among the descriptors from a huge list of exhaustive search. It shold be noted that our method makes full use of the similarity of the given data
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