# Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis

https://mdr.nims.go.jp/datasets/a0bdd56f-05f1-4670-90fa-6a046ff43ef1

## File

- [Kimoto(2024)SciRep.pdf](https://mdr.nims.go.jp/filesets/0e32839c-81a7-48aa-8524-c45de65c6a54/download) ([Detail](https://mdr.nims.go.jp/filesets/0e32839c-81a7-48aa-8524-c45de65c6a54.md))

## Id

a0bdd56f-05f1-4670-90fa-6a046ff43ef1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-02-14T03:04:46.687722Z

## Updated at

2024-02-15T07:30:11.076524Z

## Published at

2024-02-15T07:30:11.415717Z

## Doi



## First published url

https://doi.org/10.1038/s41598-024-53289-5

## Date published

2024-02-05

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Unsupervised machine learning combined with 4D scanning transmission electron
    microscopy for bimodal nanostructural analysis
  title_type: original
  lang: en

## Description

- description: Unsupervised machine learning techniques have been combined with scanning
    transmission electron microscopy (STEM) to enable comprehensive crystal structure
    analysis with nanometer spatial resolution. In this study, we investigated large-scale
    data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques
    such as non-negative matrix factorization (NMF) and hierarchical clustering with
    various optimization methods. We developed software scripts incorporating knowledge
    of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical
    clustering. Hierarchical clustering was performed using cross-correlation instead
    of conventional Euclidean distances, resulting in rotation-corrected diffractions
    and shift-corrected maps of major components. An experimental analysis was conducted
    on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix
    and crystalline precipitates with an average diameter of approximately 7 nm, which
    were challenging to detect using conventional STEM techniques. Combining 4D-STEM
    and optimized unsupervised machine learning enables comprehensive bimodal (i.e.,
    spatial and reciprocal) analyses of material nanostructures.
  description_type: abstract
  lang: und

## Creator

- name: Koji Kimoto
  role: author
  orcid: https://orcid.org/0000-0002-3927-0492
  organization: National Institute for Materials Science (NIMS)
  department: Center for Basic Research On Materials
- name: Jun Kikkawa
  role: author
  orcid: https://orcid.org/0000-0003-0659-1844
  organization: National Institute for Materials Science (NIMS)
  department: Center for Basic Research On Materials
- name: Koji Harano
  role: author
  orcid: https://orcid.org/0000-0001-6800-8023
  organization: National Institute for Materials Science (NIMS)
  department: Center for Basic Research On Materials
- name: Ovidiu Cretu
  role: author
  orcid: https://orcid.org/0000-0002-1822-8172
  organization: National Institute for Materials Science (NIMS)
  department: Center for Basic Research On Materials
- name: Yuki Shibazaki
  role: author
  orcid: https://orcid.org/0000-0002-0550-6719
- name: Fumihiko Uesugi
  role: author
  orcid: https://orcid.org/0000-0003-3346-4218
  organization: National Institute for Materials Science (NIMS)
  department: Research Network and Facility Service Division

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Scanning Transmission Electron Microscopy
  schema: not_defined
- subject: Unsupervised Machine Learning
  schema: not_defined
- subject: 4D-STEM
  schema: not_defined
- subject: Non-negative Matrix Factorization
  schema: not_defined
- subject: Hierachical Clustering
  schema: not_defined
- subject: Metallic Glass
  schema: not_defined

## Rights

- description: " This article is licensed under a Creative Commons Attribution 4.0
    International License, which permits use, sharing, adaptation, distribution and
    reproduction in any medium or format, as long as you give appropriate credit to
    the original author(s) and the source, provide a link to the Creative Commons
    licence, and indicate if changes were made. The images or other third party material
    in this\r\narticle are included in the article’s Creative Commons licence, unless
    indicated otherwise in a credit line to the material. If material is not included
    in the article’s Creative Commons licence and your intended use is not permitted
    by statutory regulation or exceeds the permitted use, you will need to obtain
    permission directly from the copyright holder. To view a copy of this licence,
    visit  http://creativecommons.org/licenses/by/4.0/."
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Scientific Reports
  issn: '20452322'
  volume: '14'
  article_number: '2901'

## Conference



## Related item



## Funding

- identifier: JP 22H05145
  funder_name: Japan Society for the Promotion of Science
- identifier: 20H02624
  funder_name: Japan Society for the Promotion of Science
- identifier: JP23H04874
  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



## Software



## Custom property



## Fileset

- id: 0e32839c-81a7-48aa-8524-c45de65c6a54
  filename: Kimoto(2024)SciRep.pdf
  content_type: application/pdf
  size: 3335621
  md5: dc774fb9c859b7200746313ce9aca4ff

## Thumbnail

fileset_id: 0e32839c-81a7-48aa-8524-c45de65c6a54
filename: Kimoto(2024)SciRep.pdf