# Intrinsic magnetic properties for SmFe 12−xTx thin films via high-throughput experiments and machine learning techniques

https://mdr.nims.go.jp/datasets/cf6059d7-0239-466f-af99-8bf4664457cf

## File

- [STAM Method Manuscript submit final.pdf](https://mdr.nims.go.jp/filesets/a8f51473-2b36-43d4-873d-bec495bfcd0c/download) ([Detail](https://mdr.nims.go.jp/filesets/a8f51473-2b36-43d4-873d-bec495bfcd0c.md))
- [STAM Method SI submit final.pdf](https://mdr.nims.go.jp/filesets/7610bb38-122d-4a33-866e-4ce58f4f0799/download) ([Detail](https://mdr.nims.go.jp/filesets/7610bb38-122d-4a33-866e-4ce58f4f0799.md))

## Id

cf6059d7-0239-466f-af99-8bf4664457cf

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-21T02:16:16.298800Z

## Updated at

2025-12-23T00:39:56.827636Z

## Published at

2025-12-23T03:19:51.639705Z

## Doi

https://doi.org/10.48505/nims.6051

## First published url

https://doi.org/10.1080/27660400.2025.2554572

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Intrinsic magnetic properties for SmFe 12−xTx thin films via high-throughput
    experiments and machine learning techniques
  title_type: original
  lang: en

## Description

- description: The development of next-generation permanent magnets has become critical
    due to the limited performance improvements in Nd-Fe-B magnets and concerns over
    rare earth supply. ThMn12-type rare-earth intermetallic compounds have emerged
    as promising alternatives, offering superior performance and reduced rare earth
    content. This study systematically investigates the magnetic properties of Sm(Fe12−xTx)-based
    thin films synthesized via combinatorial sputtering. Various stabilizing elements
    (e.g., Ti, V, Co, Cr) were analyzed to explore their effects on phase stability,
    saturation magnetization (μ0Ms), anisotropy field (μ0Hs), and Curie temperature
    (Tc). High-throughput structural and magnetic characterizations, coupled with
    machine learning (ML) predictions, facilitated efficient data acquisition and
    analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with
    Co and phase-stabilization capabilities of Ti and V. Novel insights into additives
    like Cr and Ta revealed potential Tc improvements. ML regression models (Random
    Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms.
    Predictive analyses successfully estimated μ0Ms trends and ThMn12 phase stability
    for unexplored compositions, enhancing the active learning framework for material
    discovery. This work highlights the synergy of combinatorial deposition, high-throughput
    data collection, and ML-assisted prediction in accelerating the exploration of
    magnetic materials. Future extensions to multi-element systems and other magnetic
    phases are expected to expedite the discovery of high-performance magnets for
    motors and energy applications.
  description_type: abstract
  lang: und

## Creator

- name: Daisuke Ogawa
  role: author
  orcid: https://orcid.org/0000-0002-4373-6435
  organization: National Institute for Materials Science
- name: Ryotaro Akagi
  role: author
- name: Keitaro Sodeyama
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729
  organization: National Institute for Materials Science
- name: Yukiko K. Takahashi
  role: author
  orcid: https://orcid.org/0000-0001-9197-7236
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: SmFe12
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2025-10-28

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '5'
  issue: '1'
  article_number: '2554572'

## Conference



## Related item



## Funding

- identifier: JPMJC22C3
  funder_name: JST CREST program
- identifier: JPMXP1122715503
  funder_name: Material Research and Development Project

## 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



## Software



## Custom property



## Fileset

- id: a8f51473-2b36-43d4-873d-bec495bfcd0c
  filename: STAM Method Manuscript submit final.pdf
  content_type: application/pdf
  size: 1261618
  md5: 43c3565d842938ccf250e56d36a9f780
- id: 7610bb38-122d-4a33-866e-4ce58f4f0799
  filename: STAM Method SI submit final.pdf
  content_type: application/pdf
  size: 167084
  md5: 9dbaceaf7b11ae51feb06df8c2b8dd86

## Thumbnail

fileset_id: a8f51473-2b36-43d4-873d-bec495bfcd0c
filename: STAM Method Manuscript submit final.pdf