Download file (6.61 MB)
Download as zip (6.38 MB)

Collection

Citation

Description:

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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Spin-orbit interactions, First-principle calculations, Superlattices, Informatics, Machine learning, Ferromagnetic materials, Multilayers, Thermoelectric effects

Date published: 2023-06-01

Publisher: AIP Publishing

Journal:

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

Funding:

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

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1063/5.0140332

Related item:

Other identifier(s):

Contact agent:

Updated at: 2024-05-25 08:30:17 +0900

Published on MDR: 2024-05-25 08:30:17 +0900