# Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis

https://mdr.nims.go.jp/datasets/b884f772-43f6-45c6-9f6f-ad8dc16aa290

## File

- [Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis.pdf](https://mdr.nims.go.jp/filesets/cb8b60cc-54cb-435e-a608-f23952a3beb3/download) ([Detail](https://mdr.nims.go.jp/filesets/cb8b60cc-54cb-435e-a608-f23952a3beb3.md))

## Id

b884f772-43f6-45c6-9f6f-ad8dc16aa290

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-23T22:12:28.729033Z

## Updated at

2024-08-30T07:31:01.350854Z

## Published at

2024-08-30T07:31:01.430007Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2024.2388016

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Machine learning prediction of the mechanical properties of injection-molded
    polypropylene through X-ray diffraction analysis
  title_type: original
  lang: en

## Description

- description: Predicting the mechanical properties of polymer materials using machine
    learning is essential for the design of next-generation of polymers. However,
    the strong relationship between the higher-order structure of polymers and their
    mechanical properties hinders the mechanical property predictions based on their
    primary structures. To incorporate information on higher-order structures into
    the prediction model, X-ray diffraction (XRD) can be used. This study proposes
    a strategy to generate appropriate descriptors from the XRD analysis of the injection-molded
    polypropylene samples, which were prepared under almost the same injection molding
    conditions. To this end, first, Bayesian spectral deconvolution is used to automatically
    create high-dimensional descriptors. Second, informative descriptors are selected
    to achieve highly accurate predictions by implementing the black-box optimization
    method using Ising machine. This approach was applied to custom-built polymer
    datasets containing data on homo- polypropylene and derived composite polymers
    with the addition of elastomers. Results show that reasonable accuracy of predictions
    for seven mechanical properties can be achieved using only XRD.
  description_type: abstract
  lang: und

## Creator

- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Kenji Nagata
  role: author
- name: Keitaro Sodeyama
  role: author
- name: Kensaku Nakamura
  role: author
- name: Toshiki Tokuhira
  role: author
- name: Satoshi Shibata
  role: author
- name: Kazuki Hammura
  role: author
- name: Hiroki Sugisawa
  role: author
- name: Masaya Kawamura
  role: author
- name: Teruki Tsurimoto
  role: author
- name: Masanobu Naito
  role: author
- name: Masahiko Demura
  role: author
  orcid: https://orcid.org/0000-0002-7308-3041
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Takashi Nakanishi
  role: author
  orcid: https://orcid.org/0000-0002-8744-782X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Polypropylene
  schema: not_defined
- subject: X-ray diffraction
  schema: not_defined
- subject: Bayesian spectral deconvolution
  schema: not_defined
- subject: Ising machine
  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: Science and Technology of Advanced Materials
  issn: '14686996'
  volume: '25'
  issue: '1'
  article_number: '2388016'

## Conference



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## Instrument



## Instrument operator



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

- id: cb8b60cc-54cb-435e-a608-f23952a3beb3
  filename: Machine learning prediction of the mechanical properties of injection-molded
    polypropylene through X-ray diffraction analysis.pdf
  content_type: application/pdf
  size: 6084567
  md5: 3b5cdc02e5a5b7ad213d28a6f466bdea

## Thumbnail

fileset_id: cb8b60cc-54cb-435e-a608-f23952a3beb3
filename: Machine learning prediction of the mechanical properties of injection-molded
  polypropylene through X-ray diffraction analysis.pdf