# Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data

https://mdr.nims.go.jp/datasets/e95483df-f00d-41ed-b7d5-3cd8a5b77a3c

## File

- [Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data.pdf](https://mdr.nims.go.jp/filesets/98617cce-faea-4b4c-92d7-fdf27a719d86/download) ([Detail](https://mdr.nims.go.jp/filesets/98617cce-faea-4b4c-92d7-fdf27a719d86.md))

## Id

e95483df-f00d-41ed-b7d5-3cd8a5b77a3c

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-11-07T06:55:57.486911Z

## Updated at

2025-11-10T07:30:53.259213Z

## Published at

2025-11-10T07:25:11.463819Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2024.2384352

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Bayesian inference for peak feature extraction and prediction of material
    property in X-ray diffraction data
  title_type: original
  lang: en

## Description

- description: "To advance the development of materials through data-driven scientific
    methods, appropriate methods for building machine learning (ML)-ready feature
    tables from measured and computed data must be established. In materials development,
    X-ray diffraction (XRD) is an effective technique for analysing crystal structures
    and other microstructural features that have information that can explain material
    properties. Therefore, the fully automated extraction of peak features from XRD
    data without the bias of an analyst is a significant challenge. This study aimed
    to establish an efficient and robust approach for constructing peak feature tables
    that follow ML standards (ML-ready) from XRD data. We challenge peak feature extraction
    in the situation where only the peak function profile is known a priori, without
    knowledge of the measurement material or crystal structure factor. We utilized
    Bayesian estimation to extract peak features from XRD data and subsequently performed
    Bayesian regression analysis with feature selection to predict the material property.
    The proposed method focused only on the tops of peaks within localized regions
    of interest (ROIs) and extracted peak features quickly and accurately. This process
    facilitated the rapid extracting of major peak features from the XRD data and
    the construction of an ML-ready feature table. We then applied Bayesian linear
    regression to the maximum energy product (\U0001D435\U0001D43B)\U0001D45A\U0001D44E\U0001D465,
    using the extracted peak features as the explanatory variable. The outcomes yielded
    reasonable and robust regression results. Thus, the findings of this study indicated
    that 004 peak height and area were important features for predicting (\U0001D435\U0001D43B)\U0001D45A\U0001D44E\U0001D465."
  description_type: abstract
  lang: und

## Creator

- name: Ryo Murakami
  role: author
  orcid: https://orcid.org/0000-0001-8585-9268
  organization: National Institute for Materials Science
- name: Taisuke T. Sasaki
  role: author
  orcid: https://orcid.org/0000-0002-5952-7638
  organization: National Institute for Materials Science
- name: Hideki Yoshikawa
  role: author
  orcid: https://orcid.org/0000-0002-7389-8865
  organization: National Institute for Materials Science
- name: Yoshitaka Matsushita
  role: author
  orcid: https://orcid.org/0000-0002-4968-8905
  organization: National Institute for Materials Science
- name: Keitaro Sodeyama
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729
  organization: National Institute for Materials Science
- name: Tadakatsu Ohkubo
  role: author
  orcid: https://orcid.org/0000-0003-3548-1951
  organization: National Institute for Materials Science
- name: Hiroshi Shinotsuka
  role: author
  orcid: https://orcid.org/0000-0001-5147-1396
  organization: National Institute for Materials Science
- name: Kenji Nagata
  role: author
  orcid: https://orcid.org/0000-0001-9894-4461
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Materials informatics
  schema: not_defined
- subject: Spectral decomposition
  schema: not_defined
- subject: Bayesian estimation
  schema: not_defined
- subject: Feature selection
  schema: not_defined
- subject: AI-ready
  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'
  article_number: '2384352'

## Conference



## Related item



## Funding

- identifier: JPMXP1122715503
  funder_name: 'MEXT Program: Data Creation and Utilization-Type 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



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## Fileset

- id: 98617cce-faea-4b4c-92d7-fdf27a719d86
  filename: Bayesian inference for peak feature extraction and prediction of material
    property in X-ray diffraction data.pdf
  content_type: application/pdf
  size: 7669271
  md5: 00b1b46536259eff5641dae63cd7a8dd

## Thumbnail

fileset_id: 98617cce-faea-4b4c-92d7-fdf27a719d86
filename: Bayesian inference for peak feature extraction and prediction of material
  property in X-ray diffraction data.pdf