Presentation Information
[1P08]Predicting and detecting spin-dependent properties from fermi surface data by Machine Learning
*Soichi Takase1, Daichi Ishikawa1, Kentaro Fuku1,2, Yoshio Miura3,4, Yasuhiko Igarashi5, Yuma Iwasaki6, Yuya Sakuraba3, Koichiro Yaji6,7, Alexandre Lira Foggiatto1, Takahiro Yamazaki1, Naoka Nagamura1,6,8, Masato Kotsugi1 (1. Department of Material Science and Technology, Tokyo University of Science, 2. Department of Chemistry, Graduate School of Science, Nagoya University, 3. Research Center for Magnetic and Spintronics Materials (CMSM), National Institute for Materials Science (NIMS), 4. Electrical Engineering and Electronics, Kyoto Institute of Technology, 5. Institute of System and Information Engineering, University of Tsukuba, 6. Center for Basic Research on Materials (CBRM), National Institute for Materials Science (NIMS), 7. Unprecedented-scale Data Analytics Center (UDAC), Tohoku University, 8. Research Institute of Electrical Communication (RIEC), Tohoku University)
The Fermi surface plays a pivotal role in determining the physical properties of solids, particularly in materials exhibiting topological or spintronic functionalities. Among these, nodal-line features on the Fermi surface have been linked to emergent phenomena such as the anomalous Hall effect (AHE), anomalous Nernst effect (ANE), and high spin polarization under external perturbations. Co-based Heusler alloys, known for their tunable electronic structures and compatibility with spintronic applications, offer an ideal platform to explore these effects. However, systematic quantitative analysis of their Fermi surfaces remains challenging due to the complexity of the band structures and the limitations of conventional manual inspection methods. In this study, we propose a framework to analyze the Fermi surface structure of Co-based Heusler alloys using machine learning, enabling the prediction of spin-dependent properties from both spin-resolved and spin-unresolved data.