# High-throughput materials exploration system for the anomalous Hall effect using combinatorial experiments and machine learning

https://mdr.nims.go.jp/datasets/382252bc-e3ce-4806-bf33-2f86549cb70a

## File

- [s41524-025-01757-5 (2).pdf](https://mdr.nims.go.jp/filesets/33f1375b-d2eb-4375-b320-d4565813e6d3/download) ([Detail](https://mdr.nims.go.jp/filesets/33f1375b-d2eb-4375-b320-d4565813e6d3.md))

## Id

382252bc-e3ce-4806-bf33-2f86549cb70a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-26T02:34:23.388761Z

## Updated at

2025-12-26T04:11:05.003093Z

## Published at

2025-12-26T07:13:50.847366Z

## Doi



## First published url

https://doi.org/10.1038/s41524-025-01757-5

## Date published

2025-09-03

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: High-throughput materials exploration system for the anomalous Hall effect
    using combinatorial experiments and machine learning
  title_type: original
  lang: en

## Description

- description: The development of new materials exhibiting large anomalous Hall effect
    (AHE) is essential for realizing highly efficient spintronic devices. However,
    this development has been a time-consuming process due to the combinatorial explosion
    for multielement systems and limited experimental throughput. In this study, we
    identify new materials exhibiting large AHE in heavy-metal-substituted Fe-based
    alloys using a high-throughput materials exploration method that combines deposition
    of composition-spread films using combinatorial sputtering, photoresist-free facile
    multiple-device fabrication using laser patterning, simultaneous AHE measurement
    of multiple devices using a customized multichannel probe, and prediction of candidate
    materials using machine learning. Based on experimental AHE data on Fe-based binary
    system alloyed with various single heavy metals, we perform machine learning analysis
    to predict the Fe-based ternary system containing two heavy metals for larger
    AHE. We experimentally confirm larger AHE in the predicted Fe–Ir–Pt system. Using
    scaling analysis, we reveal that the enhancement of AHE originates from the extrinsic
    contribution.
  description_type: abstract
  lang: und

## Creator

- name: Ryo Toyama
  role: author
  orcid: https://orcid.org/0000-0002-7398-5803
- name: Yuma Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7117-277X
- name: Prabhanjan D. Kulkarni
  role: author
  orcid: https://orcid.org/0000-0002-4605-5256
- name: Hirofumi Suto
  role: author
  orcid: https://orcid.org/0000-0003-4387-5862
- name: Tomoya Nakatani
  role: author
  orcid: https://orcid.org/0000-0001-9590-216X
- name: Yuya Sakuraba
  role: author
  orcid: https://orcid.org/0000-0003-4618-9550

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Machine learning
  schema: not_defined
- subject: High-throughput
  schema: not_defined
- subject: Combinatorial
  schema: not_defined
- subject: Anomalous Hall effect
  schema: not_defined

## Rights

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

## Other identifier(s)



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## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '11'
  issue: '1'
  article_number: '269'

## Conference



## Related item



## Funding

- identifier: JPMJCR21O1
  funder_name: Japan Science and Technology Agency
- identifier: JPMJCR21O1
  funder_name: Japan Science and Technology Agency
- identifier: JP24K00932
  funder_name: Japan Society for the Promotion of Science
- identifier: JP21H01608
  funder_name: Japan Society for the Promotion of Science
- identifier: JPMXP1122715503
  funder_name: Ministry of Education, Culture, Sports, Science and Technology

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## Fileset

- id: 33f1375b-d2eb-4375-b320-d4565813e6d3
  filename: s41524-025-01757-5 (2).pdf
  content_type: application/pdf
  size: 2214037
  md5: e005af5157282c9075259afd7e305b22

## Thumbnail

fileset_id: 33f1375b-d2eb-4375-b320-d4565813e6d3
filename: s41524-025-01757-5 (2).pdf