Important descriptors and descriptor groups of Curie temperatures of rare-earth transition-metal binary alloys

ORAL

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 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.
arXiv: 1809.04750.

*This work was partly supported by PRESTO and by the "Materials Research by Information Integration" Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub, both from the Japan Science and Technology Agency (JST), Japan; by the Elements Strategy Initiative Project under the auspices of MEXT; and also by MEXT as a social and scientific priority issue (Creation of New Functional Devices and High-Performance Materials to Support Next-Generation Industries; CDMSI) to be tackled by using a post-K computer. The calculations are partly carried out on Numerical Materials Simulator at NIMS.

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Presenters

  • Hiori Kino

    • National Institute for Materials Science

Authors

  • Hiori Kino

    • National Institute for Materials Science