# Autonomous search for materials with high Curie temperature using            <i>ab initio</i>            calculations and machine learning

https://mdr.nims.go.jp/datasets/a127ace5-c92a-4ebb-b136-a44c45f3da07

## File

- [Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning (1).pdf](https://mdr.nims.go.jp/filesets/f24d378d-18a4-4e88-b972-f1ca7121da46/download) ([Detail](https://mdr.nims.go.jp/filesets/f24d378d-18a4-4e88-b972-f1ca7121da46.md))

## Id

a127ace5-c92a-4ebb-b136-a44c45f3da07

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-03T05:15:08.761148Z

## Updated at

2024-10-03T23:30:28.358992Z

## Published at

2024-10-03T23:30:28.519382Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2024.2399494

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Autonomous search for materials with high Curie temperature using            <i>ab
    initio</i>            calculations and machine learning
  title_type: original
  lang: en

## Description

- description: Efficient exploration of vast material spaces is a challenging task
    in materials science. Autonomous material search methods utilizing machine learning
    and ab initio calculations have emerged as powerful alternatives to traditional
    material discovery through synthesis and analysis, which is time-consuming and
    scope-limited. Although autonomous search methods have already been applied to
    various material spaces, they have not explored the extensive material space of
    Curie temperatures. Herein, we show a simulation-based autonomous search method
    that suggests ternary alloys with high Curie temperatures. The material space—consisting
    of disordered ternary magnetic alloys—is explored through Korringa–Kohn–Rostoker
    coherent potential approximation and Bayesian optimization. Over a continuous
    10-day search, the system proposed several alloys—CoAuIr, CoPtPd, and CoFeBi—with
    Curie temperatures surpassing that of pure face-centered cubic Co. Although the
    insights gained through these predictions require further experi
  description_type: abstract
  lang: und

## Creator

- name: Yuma Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7117-277X

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Machine learning
  schema: not_defined
- subject: Autonomous
  schema: not_defined
- subject: Curie temperature
  schema: not_defined
- subject: ab initio
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '4'
  issue: '1'

## Conference



## Related item



## Funding

- identifier: JPMJCR21O1
  funder_name: Japan Science and Technology Corporation

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



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## Fileset

- id: f24d378d-18a4-4e88-b972-f1ca7121da46
  filename: Autonomous search for materials with high Curie temperature using ab initio
    calculations and machine learning (1).pdf
  content_type: application/pdf
  size: 4231897
  md5: 5132673ab2cc8906c05e9ec42ffd355c

## Thumbnail

fileset_id: f24d378d-18a4-4e88-b972-f1ca7121da46
filename: Autonomous search for materials with high Curie temperature using ab initio
  calculations and machine learning (1).pdf