Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties

Research output: Contribution to journalArticleResearchpeer review

Authors

  • A. A. Catal
  • E. Bedir
  • R. Yilmaz
  • M. A. Swider
  • C. Lee
  • O. El-Atwani
  • H. J. Maier
  • H. C. Ozdemir
  • D. Canadinc

Research Organisations

External Research Organisations

  • Koc University
  • Eskişehir Technical University
  • Los Alamos National Laboratory Materials Science and Technology Division
  • Auburn University
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Details

Original languageEnglish
Article number112612
JournalComputational materials science
Volume231
Early online date31 Oct 2023
Publication statusPublished - 5 Jan 2024

Abstract

This paper details an alloy design effort by machine learning (ML) attempting to design novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at elevated temperatures and good room temperature ductility. For this purpose, four datasets were generated by mining the data available in literature, containing room temperature strength, high temperature strength, room temperature ductility and hardness, which were trained by three different ML models, namely the support vector regression, random forest, and artificial neural network. As a result, three novel RHEA compositions were predicted, and their performances were experimentally validated. Specifically, the Ti8Nb21Zr27Ta13Mo19V12, Ti10Nb19Zr15Ta43Mo7V6, and Ti10Nb20Zr37Mo21V12 RHEAs were produced and subjected to room-temperature and high-temperature compression, and room-temperature hardness tests, which have demonstrated that especially the Ti8Nb21Zr27Ta13Mo19V12 and the Ti10Nb20Zr37Mo21V12 RHEAs exhibit both high strength at elevated temperatures and good room-temperature ductility. The current study not only contributes to the literature by presenting three novel RHEAs, but also constitutes an encouraging example of efficient alloy design by ML for demanding applications.

Keywords

    Alloy design, Ductility, High-temperature strength, Machine learning, Refractory high entropy alloy

ASJC Scopus subject areas

Cite this

Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties. / Catal, A. A.; Bedir, E.; Yilmaz, R. et al.

In: Computational materials science, Vol. 231, 112612, 05.01.2024.

Research output: Contribution to journalArticleResearchpeer review

Catal, A. A., Bedir, E., Yilmaz, R., Swider, M. A., Lee, C., El-Atwani, O., Maier, H. J., Ozdemir, H. C., & Canadinc, D. (2024). Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties. Computational materials science, 231, [112612]. https://doi.org/10.1016/j.commatsci.2023.112612
Catal AA, Bedir E, Yilmaz R, Swider MA, Lee C, El-Atwani O et al. Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties. Computational materials science. 2024 Jan 5;231:112612. Epub 2023 Oct 31. doi: 10.1016/j.commatsci.2023.112612
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abstract = "This paper details an alloy design effort by machine learning (ML) attempting to design novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at elevated temperatures and good room temperature ductility. For this purpose, four datasets were generated by mining the data available in literature, containing room temperature strength, high temperature strength, room temperature ductility and hardness, which were trained by three different ML models, namely the support vector regression, random forest, and artificial neural network. As a result, three novel RHEA compositions were predicted, and their performances were experimentally validated. Specifically, the Ti8Nb21Zr27Ta13Mo19V12, Ti10Nb19Zr15Ta43Mo7V6, and Ti10Nb20Zr37Mo21V12 RHEAs were produced and subjected to room-temperature and high-temperature compression, and room-temperature hardness tests, which have demonstrated that especially the Ti8Nb21Zr27Ta13Mo19V12 and the Ti10Nb20Zr37Mo21V12 RHEAs exhibit both high strength at elevated temperatures and good room-temperature ductility. The current study not only contributes to the literature by presenting three novel RHEAs, but also constitutes an encouraging example of efficient alloy design by ML for demanding applications.",
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note = "Funding Information: D. Canadinc acknowledges the support by the Alexander von Humboldt Foundation (Germany) within the scope of Humboldt Research Award. H.J. Maier acknowledges financial support by Deutsche Forschungsgemeinschaft ( project #388671975 ) (Germany). ",
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AU - Bedir, E.

AU - Yilmaz, R.

AU - Swider, M. A.

AU - Lee, C.

AU - El-Atwani, O.

AU - Maier, H. J.

AU - Ozdemir, H. C.

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