# Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN

https://mdr.nims.go.jp/datasets/dbab871a-d7d4-4831-a48c-12c483d2950a

## File

- [Wu 2025 J Appl Crystallogr nanoXRD UMAP HVPE GaN.pdf](https://mdr.nims.go.jp/filesets/e1743720-5a5e-4e54-ae57-b30dfcf547e9/download) ([Detail](https://mdr.nims.go.jp/filesets/e1743720-5a5e-4e54-ae57-b30dfcf547e9.md))

## Id

dbab871a-d7d4-4831-a48c-12c483d2950a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-07-30T07:20:32.181721Z

## Updated at

2025-07-31T03:30:18.820175Z

## Published at

2025-07-31T03:16:53.846934Z

## Doi



## First published url

https://doi.org/10.1107/s1600576725004169

## Date published

2025-08-01

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Machine learning assisted nanobeam X-ray diffraction based analysis on hydride
    vapor-phase epitaxy GaN
  title_type: original
  lang: en

## Description

- description: "Nanobeam X-ray diffraction (nanoXRD) is a powerful tool for collecting
    in situ crystal structure information with high spatial resolution and data acquisition
    rate. However, analyzing the enormous amount of data produced by these high-throughput
    experiments for defect recognition or discovering hidden structural features becomes
    challenging. Machine learning (ML) methods have become attractive recently due
    to their outstanding performance in analyzing large data\r\nsets. This research
    utilizes an ML algorithm, uniform manifold approximation and projection (UMAP),
    to enhance the nanoXRD-based crystal structure analysis of a cross-sectional hydride
    vapor-phase epitaxy GaN wafer."
  description_type: abstract
  lang: und

## Creator

- name: Zhendong Wu
  role: author
- name: Yusuke Hayashi
  role: author
  orcid: https://orcid.org/0000-0001-5672-1497
  organization: National Institute for Materials Science
- name: Tetsuya Tohei
  role: author
- name: Kazushi Sumitani
  role: author
- name: Yasuhiko Imai
  role: author
- name: Shigeru Kimura
  role: author
- name: Akira Sakai
  role: author

## Contact agent



## Publisher

organization: International Union of Crystallography (IUCr)

## Managing organization



## Keyword

- subject: GaN
  schema: not_defined
- subject: SPring-8
  schema: not_defined
- subject: nanoXRD
  schema: not_defined
- subject: Machine learning
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Journal of Applied Crystallography
  issn: '16005767'
  volume: '58'
  issue: '4'
  article_number: S1600576725004169

## Conference



## Related item



## Funding

- identifier: JP16H06423
  funder_name: Japan Society for the Promotion of Science
- identifier: JP20H00352
  funder_name: Japan Society for the Promotion of Science
- identifier: JP22KK0055
  funder_name: Japan Society for the Promotion of Science
- identifier: JP23H01447
  funder_name: Japan Society for the Promotion of Science
- funder_name: Murata Science and Education Foundation

## 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: e1743720-5a5e-4e54-ae57-b30dfcf547e9
  filename: Wu 2025 J Appl Crystallogr nanoXRD UMAP HVPE GaN.pdf
  content_type: application/pdf
  size: 12116436
  md5: ff09f610a9bcaddca15c223e86c1eec3

## Thumbnail

fileset_id: e1743720-5a5e-4e54-ae57-b30dfcf547e9
filename: Wu 2025 J Appl Crystallogr nanoXRD UMAP HVPE GaN.pdf