# Designing composition ratio of magnetic alloy multilayer for transverse thermoelectric conversion by Bayesian optimization

https://mdr.nims.go.jp/datasets/6b275e2b-49e0-4c2d-a177-ff5056315483

## File

- [2023Chiba_AML_026114_1_5.0140332.pdf](https://mdr.nims.go.jp/filesets/ba2eed02-81ba-4f9d-8e10-de855f5d1006/download) ([Detail](https://mdr.nims.go.jp/filesets/ba2eed02-81ba-4f9d-8e10-de855f5d1006.md))

## Id

6b275e2b-49e0-4c2d-a177-ff5056315483

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-05-24T02:18:53.292768Z

## Updated at

2024-05-24T23:30:17.437269Z

## Published at

2024-05-24T23:30:17.564152Z

## Doi



## First published url

https://doi.org/10.1063/5.0140332

## Date published

2023-06-01

## Recorded date published

2023-6-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Designing composition ratio of magnetic alloy multilayer for transverse thermoelectric
    conversion by Bayesian optimization
  title_type: original
  lang: en

## Description

- 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\r\n 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."
  description_type: abstract
  lang: en

## Creator

- name: Naoki Chiba
  role: author
  orcid: https://orcid.org/0009-0003-0296-9026
  organization: National Institute for Materials Science
- name: Keisuke Masuda
  role: author
  orcid: https://orcid.org/0000-0002-6884-6390
  organization: National Institute for Materials Science
- name: Ken-ichi Uchida
  role: author
  orcid: https://orcid.org/0000-0001-7680-3051
  organization: National Institute for Materials Science
- name: Yoshio Miura
  role: author
  orcid: https://orcid.org/0000-0002-5605-5452
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: AIP Publishing

## Managing organization



## Keyword

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

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: APL Machine Learning
  issn: '27709019'
  volume: '1'
  issue: '2'
  article_number: '026114'

## Conference



## Related item



## Funding

- identifier: JPMJCR17I1
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJER2201
  funder_name: Exploratory Research for Advanced Technology
- identifier: JP16H06332
  funder_name: Japan Society for the Promotion of Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



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## Custom property



## Fileset

- id: ba2eed02-81ba-4f9d-8e10-de855f5d1006
  filename: 2023Chiba_AML_026114_1_5.0140332.pdf
  content_type: application/pdf
  size: 6930670
  md5: a3bf12ae0c7108db8d51daf736700977

## Thumbnail

fileset_id: ba2eed02-81ba-4f9d-8e10-de855f5d1006
filename: 2023Chiba_AML_026114_1_5.0140332.pdf