[25a-61C-1~10] 23.1 Joint Session N "Informatics"

9:30 AM - 9:45 AM JST(12:30 AM - 12:45 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[25a-61C-3] Development of an automatic analysis method for Fermi surfaces based on data science

〇(B)Daichi Ishikawa¹, Kentaro Fuku¹, Yoshio Miura², Yasuhiko Igarashi³, Yuma Iwasaki², Yuya Sakuraba², Koichiro Yaji², Alexandre Lira Foggiatto¹, Arpita Varadwaj¹, Naoka Nagamura², Masato Kotsugi¹ (1.Tokyo Univ. of Sci., 2.NIMS, 3.Tsukuba Univ.)

Keywords:

fermi surface,spin polarization,machine learning

Fermi surfaces are an important source of information that determines the applied functions of spintronics devices, and there are high expectations for the automatic extraction of information on physical properties from Fermi surfaces. From this point of view, we applied machine learning to Fermi surfaces, which change in a complex composition-dependent manner, and designed a model to automatically visualize the regions that contribute to the expression of functions. The model was applied to CMGG as a material system, and as a result, we were able to establish a relationship between the shape change of the Fermi surface and the spin polarization rate in the data space.