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

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Wang_K_2022_T.pdf (1.28 MB)

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2022-08

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Springer Nature

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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This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s42864-022-00169-y

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Yan, YG., Wang, K. The intrinsic strength prediction by machine learning for refractory high entropy alloys. Tungsten (2022). https://doi.org/10.1007/s42864-022-00169-y

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http://hdl.handle.net/10829/30732

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