# Advancing refractory high entropy alloy development with AI-predictive models for high temperature oxidation resistance

https://mdr.nims.go.jp/datasets/e55306e0-6429-44c0-9e81-ae890b427ee7

## Files

- [1-s2.0-S1359646224004299-main.pdf](https://mdr.nims.go.jp/filesets/8168a614-fe6d-4acc-ac06-f3b4b46ae02e/download) ([Detail](https://mdr.nims.go.jp/filesets/8168a614-fe6d-4acc-ac06-f3b4b46ae02e.md))

## Id

e55306e0-6429-44c0-9e81-ae890b427ee7

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-07T01:45:55.297422Z

## Updated at

2024-10-08T23:30:56.824596Z

## Published at

2024-10-08T23:30:56.879570Z

## Doi



## First published url

https://doi.org/10.1016/j.scriptamat.2024.116394

## Date published

2024-10-01

## Recorded date published

2025-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Advancing refractory high entropy alloy development with AI-predictive models
    for high temperature oxidation resistance
  title_type: original
  lang: en

## Description

- description: Refractory high-entropy alloys (RHEAs) and complex concentrated alloys
    (RCCAs) are vital for high-temperature applications beyond the capabilities of
    Ni-based superalloys. Traditional methods for predicting oxidation resistance
    in these alloys are often inaccurate and resource-intensive. This study introduces
    a novel approach using Gradient Boosted Decision Trees (GBDT), an artificial intelligence
    technique, to predict specific mass gain due to oxidation. Utilizing a dataset
    synthesized from extensive literature and characterized by diverse alloy compositions
    and oxidation conditions, the model was trained using Iterated Nested k-fold Cross
    Validation with Shuffling (INKCVS). Our findings demonstrate that the GBDT model
    achieves a good balance between accuracy and generalization capacity in predicting
    oxidation resistance, as validated experimentally with selected alloys. This approach
    not only enhances prediction accuracy but also significantly reduces the need
    for extensive experimental testing, facilitating rapid development of new high-performance
    materials.
  description_type: abstract
  lang: und

## Creator

- name: Stéphane Gorsse
  role: author
  orcid: https://orcid.org/0000-0003-1966-8476
- name: Wei-Chih Lin
  role: author
- name: Hideyuki Murakami
  role: author
  orcid: https://orcid.org/0000-0001-8220-5816
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Gian-Marco Rignanese
  role: author
  orcid: https://orcid.org/0000-0002-1422-1205
- name: An-Chou Yeh
  role: author
  orcid: https://orcid.org/0000-0002-9460-8345

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Refractory high entropy alloys
  schema: not_defined
- subject: Oxidation resistance
  schema: not_defined
- subject: AI model
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: SCRIPTA MATERIALIA
  issn: '13596462'
  volume: '255'
  article_number: '116394'

## Conference



## Related item



## Funding

- funder_name: Centre National de la Recherche Scientifique
- identifier: ANR-22-PEXD-0003
  funder_name: Agence Nationale de la Recherche

## Instrument



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## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



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## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

- id: 8168a614-fe6d-4acc-ac06-f3b4b46ae02e
  filename: 1-s2.0-S1359646224004299-main.pdf
  content_type: application/pdf
  size: 6834547
  md5: d69527421aae3be250521ee9f7143214

## Thumbnail

fileset_id: 8168a614-fe6d-4acc-ac06-f3b4b46ae02e
filename: 1-s2.0-S1359646224004299-main.pdf