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The intrinsic strength prediction by machine learning for refractory high entropy alloys

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Abstract

Herein, we trained machine learning (ML) model to quickly and accurately conduct the strength prediction of refractory high entropy alloys (RHEAs) matrix. Gradient Boosting (GB) regression model shows an outstanding performance against other ML models. In addition, the heat of fusion and atomic size difference is shown to be paramount to the strength of the high entropy alloys (HEAs) matrix. In addition, we discussed the contribution of each feature to the solid solution strengthening (SSS) of HEAs. The excellent predictive accuracy shows that the GB model can be efficient and reliable for the design of RHEAs with desired strength.

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The data and methods reported in this paper are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by the Faculty Startup Fund in the New York State College of Ceramics at Alfred University.

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  1. Kazuo Inamori School of Engineering, Alfred University, Alfred, NY, 14802, USA

    Yong-Gang Yan & Kun Wang

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  1. Yong-Gang Yan
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Yan, YG., Wang, K. The intrinsic strength prediction by machine learning for refractory high entropy alloys. Tungsten 5, 531–538 (2023). https://doi.org/10.1007/s42864-022-00169-y

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