# Evidence-based recommender system for high-entropy alloys

https://mdr.nims.go.jp/datasets/86470b94-2403-45b2-a115-636a1ac7cc33

## File

- [s43588-021-00097-w.pdf](https://mdr.nims.go.jp/filesets/14415fa0-c15a-4e0e-a052-c99d029f6c3f/download) ([Detail](https://mdr.nims.go.jp/filesets/14415fa0-c15a-4e0e-a052-c99d029f6c3f.md))

## Id

86470b94-2403-45b2-a115-636a1ac7cc33

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-08T02:07:08.453199Z

## Updated at

2024-01-05T13:12:38.971332Z

## Published at

2023-02-09T02:13:47.027999Z

## Doi



## First published url

https://doi.org/10.1038/s43588-021-00097-w

## Date published

2021-07-19

## Recorded date published



## Resource type

journal_article

## Manuscript type

na

## Collection



## Title

- title: Evidence-based recommender system for high-entropy alloys
  title_type: original
  lang: en

## Description

- description: "Existing data-driven approaches for exploring high-entropy alloys
    (HEAs) face three challenges: numerous element-combination candidates, designing
    appropriate descriptors, and limited and biased existing data. To overcome these
    issues, here we \r\nshow the development of an evidence-based material recommender
    system (ERS) that adopts Dempster–Shafer theory, a general framework for reasoning
    with uncertainty. Herein, without using material descriptors, we model, collect
    and combine pieces \r\nof evidence from data about the HEA phase existence of
    alloys. To evaluate the ERS, we compared its HEA-recommendation \r\ncapability
    \ with  those  of  matrix-factorization-  and  supervised-learning-based  recommender
    \ systems  on  four  widely  known \r\ndatasets of up-to-five-component alloys.
    The k-fold cross-validation on the datasets suggests that the ERS outperforms
    all \r\ncompetitors. Furthermore, the ERS shows good extrapolation capabilities
    in recommending quaternary and quinary HEAs. We \r\nexperimentally validated the
    most strongly recommended Fe–Co-based magnetic HEA (namely, FeCoMnNi) and confirmed
    that \r\nits thin film shows a body-centered cubic structure."
  description_type: abstract
  lang: eng

## Creator

- name: Minh-Quyet Ha
  role: author
- name: Duong-Nguyen Nguyen
  role: author
- name: Viet-Cuong Nguyen
  role: author
- name: Takahiro Nagata
  role: author
  orcid: https://orcid.org/0000-0002-8591-2943
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Toyohiro Chikyow
  role: author
  orcid: https://orcid.org/0000-0003-3860-4806
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Hiori Kino
  role: author
  orcid: https://orcid.org/0000-0002-8912-686X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Takashi Miyake
  role: author
- name: Thierry Denœux
  role: author
- name: Van-Nam Huynh
  role: author
- name: Hieu-Chi Dam
  role: author

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## Keyword

- subject: Dempster–Shafer theory
  schema: not_defined
- subject: evidence theory
  schema: not_defined
- subject: high-entropy alloys
  schema: not_defined
- subject: recommender system
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

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## Journal

- title: Nature Computational Science
  issn: '26628457'
  volume: '1'
  issue: '7'
  start_page: 470
  end_page: 478

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## Related item



## Funding

- identifier: '12016013'
  funder_name: MEXT ESICMM
- identifier: DPMSD
  funder_name: the Program for Promoting  Research on the Supercomputer Fugaku
- identifier: JPMJMI18G5
  funder_name: JST-Mirai
- identifier: 20K05301
  funder_name: JSPS KAKENHI
- identifier: JP19H05815
  funder_name: Grants-in-Aid  for Scientific Research on Innovative Areas Interface
    Ionics
- identifier: 20K05068
  funder_name: JSPS KAKENHI
- identifier: 20K05301
  funder_name: JSPS KAKENHI

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## Fileset

- id: 14415fa0-c15a-4e0e-a052-c99d029f6c3f
  filename: s43588-021-00097-w.pdf
  content_type: application/pdf
  size: 2203171
  md5: 21990e6f78c3977942b39328ce573419

## Thumbnail

fileset_id: 14415fa0-c15a-4e0e-a052-c99d029f6c3f
filename: s43588-021-00097-w.pdf