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説明:

Wedemonstrated the effectiveness of the machine learning method combined with first-principles calculations for the enhancement of the anomalous Nernst effect (ANE) of multilayers. The composition ratio of CoNi homogeneous alloy superlattices was optimized by Bayesian
optimization so as to maximize the transverse thermoelectric conductivity (αxy). The nonintuitive optimal composition with a large αxy of ∼10 A K−1 m−1 was identified through the two-step Bayesian optimization using rough and fine candidate pools. The Berry curvature and band dispersion analyses revealed that αxy is enhanced by the appearance of the flat band near the Fermi level due to the multilayer formation. The magnitude of the energy derivative of the anomalous Hall conductivity increases owing to the large Berry curvature near the flat band along the R-M high symmetry line, which emerges only in the optimized superlattice, leading to the αxy enhancement. The effective method verified here will broaden the choices of ANE materials to more complex systems and, therefore, lead to the development of transverse thermoelectric conversion technologies.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Spin-orbit interactions, First-principle calculations, Superlattices, Informatics, Machine learning, Ferromagnetic materials, Multilayers, Thermoelectric effects

刊行年月日: 2023-06-01

出版者: AIP Publishing

掲載誌:

• APL Machine Learning (ISSN: 27709019) vol. 1 issue. 2 026114

研究助成金:

• Core Research for Evolutional Science and Technology JPMJCR17I1
• Exploratory Research for Advanced Technology JPMJER2201
• Japan Society for the Promotion of Science JP16H06332

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1063/5.0140332

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更新時刻: 2024-05-25 08:30:17 +0900

MDRでの公開時刻: 2024-05-25 08:30:17 +0900