# Quantum Annealing Optimization Method for the Design of Barrier Materials in Magnetic Tunnel Junctions

https://mdr.nims.go.jp/datasets/d015ccbd-cbff-4ef3-a2e9-be1cc5c56816

## File

- [2023Nawa_QuantumAnnealing_PhysRevApplied.20.024044.pdf](https://mdr.nims.go.jp/filesets/07f6cb86-2ea8-474c-b446-4f43148bc023/download) ([Detail](https://mdr.nims.go.jp/filesets/07f6cb86-2ea8-474c-b446-4f43148bc023.md))

## Id

d015ccbd-cbff-4ef3-a2e9-be1cc5c56816

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-05-24T02:12:48.058825Z

## Updated at

2024-05-24T23:30:14.563498Z

## Published at

2024-05-24T23:30:14.681247Z

## Doi



## First published url

https://doi.org/10.1103/physrevapplied.20.024044

## Date published

2023-08-17

## Recorded date published

2023-8

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Quantum Annealing Optimization Method for the Design of Barrier Materials
    in Magnetic Tunnel Junctions
  title_type: original
  lang: en

## Description

- description: "In the field of spintronics, there is a strong demand for barrier
    materials in magnetic tunnel junctions (MTJs) having high tunnel magnetoresistance
    (TMR) and low resistance area product (RA). However, the design of such barrier
    materials with atomically controlled composition, vacancies, and disordering of
    the constituent elements is still challenging due to the combinatorial explosion
    of potential candidates. Very recently, a quantum annealing (QA) method has been
    utilized for combinatorial optimization problems in materials science. In this
    paper, we perform a proof-of-concept study by applying the QA approach combining
    with first-principles calculations and a machine-learning factorization machine
    (FM) to MTJs with inverse-type spinel MgGa2O4 tunnel barrier. We treat 252 combinations
    of Mg2+ and Ga3+ cation disordering in the barrier layer of the MTJs and discuss
    the effect of the cation disordering on the structural stability, TMR, and RA.
    Our method is superior to simulated annealing, Bayesian optimization, and\r\nrandom
    sampling in searching for the best MTJs for low total energy and high TMR, but
    not so for low RA. We also revealed physical origins of high TMR and low RA behind
    the cation disordering. The present work highlights the applicability and advantage
    of materials informatics using FM+QA with first-principles calculations in designing
    spintronic MTJ devices."
  description_type: abstract
  lang: en

## Creator

- name: Kenji Nawa
  role: author
  orcid: https://orcid.org/0000-0003-4535-0920
  organization: National Institute for Materials Science
- name: Tsuyoshi Suzuki
  role: author
- name: Keisuke Masuda
  role: author
  orcid: https://orcid.org/0000-0002-6884-6390
  organization: National Institute for Materials Science
- name: Shu Tanaka
  role: author
- name: Yoshio Miura
  role: author
  orcid: https://orcid.org/0000-0002-5605-5452
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: American Physical Society (APS)

## Managing organization



## Keyword

- subject: Magnetic tunnel junctions, Adiabatic quantum optimization, Quantum computation,
    Spintronics
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Physical Review Applied
  issn: '23317019'
  volume: '20'
  issue: '2'
  article_number: '024044'

## Conference



## Related item



## Funding

- funder_name: National Institute of Advanced Industrial Science and Technology
- funder_name: NIMS
- identifier: JP20H02190
  funder_name: KAKENHI
- identifier: JP20K14782
  funder_name: KAKENHI
- identifier: JP21H01750
  funder_name: KAKENHI
- identifier: JP21K03391
  funder_name: KAKENHI
- identifier: JP22H04966
  funder_name: KAKENHI
- funder_name: Numerical Materials Simulator
- identifier: JP22K14290
  funder_name: KAKENHI
- identifier: JP23H05447
  funder_name: KAKENHI
- identifier: JP23K03933
  funder_name: KAKENHI
- funder_name: MEXT
- funder_name: TDK Corporation
- funder_name: Data-Science Research Center
- funder_name: Material, Quantum, and Measurement Technologies, Mie University

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## Fileset

- id: 07f6cb86-2ea8-474c-b446-4f43148bc023
  filename: 2023Nawa_QuantumAnnealing_PhysRevApplied.20.024044.pdf
  content_type: application/pdf
  size: 8518164
  md5: a4d4e053ca135e8544a9b068a6ec9587

## Thumbnail

fileset_id: 07f6cb86-2ea8-474c-b446-4f43148bc023
filename: 2023Nawa_QuantumAnnealing_PhysRevApplied.20.024044.pdf