Machine learning-assisted design of refractory high-entropy alloys with targeted yield strength and fracture strain

• A machine learning system was used to predict yield strength and fracture strain of RHEAs under compression. • The machine learning system performs better in predicting the yield strength and fracture strain. • Four RHEAs with higher yield strength or superior fracture strain in the Nb-Ta-Ti-V-W RHEAs were synthesized. • Decreasing mixing enthalpy and increasing electronegativity difference can promote the formation of precipitated phase. In order to improve the traditional “trial and error” material design method, machine learning-yield strength and machine learning-fracture strain models are incorporated into one system to predict yield strength and fracture strain in refractory high-entropy alloys (RHEAs) under compression. The ML-yield strength model and ML-fracture strain model achieve excellent predictions (R 2 = 0.942, RMSE=0.35) and (R 2 = 0.892, RMSE=0.41) in the testing set, respectively. Based on the machine learning model, Nb 0.22 Ta 0.22 Ti 0.24 V 0.23 W 0.09 , Nb 0.24 Ta 0.22 Ti 0.26 V 0.04 W 0.24 , Nb 0.26 Ta 0.24 Ti 0.21 V 0.24 W 0.05 , and Nb 0.18 Ta 0.26 Ti 0.22 V 0.21 W 0.13 RHEAs in the Nb-Ta-Ti-V-W RHEA system were screened and synthesized. The yield strength (1915 MPa, 1983 MPa) of the Nb 0.22 Ta 0.22 Ti 0.24 V 0.23 W 0.09 and Nb 0.24 Ta 0.22 Ti 0.26 V 0.04 W 0.24 RHEAs are higher than that (1689 MPa) of the NbTaTiVW RHEA. The unfractured Nb 0.18 Ta 0.26 Ti 0.22 V 0.21 W 0.13 and Nb 0.26 Ta 0.24 Ti 0.21 V 0.24 W 0.05 RHEAs under compression exhibit superior performance than the fracture strain (16.6 %) of the NbTaTiVW RHEA. The mixing enthalpy of RHEAs is negatively correlated with the yield strength, whereas a negative relationship exists between electronegativity difference and fracture strain through the SHAP analysis. Decreasing the mixing enthalpy and increasing the electronegativity difference promote the formation of the precipitated phase. The electron probe microanalysis reveals that the differences in mechanical properties (yield strength and fracture strain) in the NbTaTiVW RHEAs primarily stem from the fraction of the precipitated phase.

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Publication Details

Journal
Materials & Design
Published
2024-09-20
DOI
https://doi.org/10.1016/j.matdes.2024.113326
Citations
38
Primary Topic
High Entropy Alloys Studies
Type
article
Field-Weighted Citation Impact
8.08

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article

Machine learning-assisted design of refractory high-entropy alloys with targeted yield strength and fracture strain

Xingwang Cheng, Pingluo Zhao, Jianye He, Fan Zhang et al.
38 citations
Materials & Design
High Entropy Alloys Studies
8.08
article

Machine learning-assisted design of refractory high-entropy alloys with targeted yield strength and fracture strain

Xingwang Cheng, Pingluo Zhao, Jianye He, Fan Zhang, Lin Wang, Zezhou Li, Hongmei Zhang, Jingchen Lin
article en
38 citations

Abstract

• A machine learning system was used to predict yield strength and fracture strain of RHEAs under compression. • The machine learning system performs better in predicting the yield strength and fracture strain. • Four RHEAs with higher yield strength or superior fracture strain in the Nb-Ta-Ti-V-W RHEAs were synthesized. • Decreasing mixing enthalpy and increasing electronegativity difference can promote the formation of precipitated phase. In order to improve the traditional “trial and error” material design method, machine learning-yield strength and machine learning-fracture strain models are incorporated into one system to predict yield strength and fracture strain in refractory high-entropy alloys (RHEAs) under compression. The ML-yield strength model and ML-fracture strain model achieve excellent predictions (R 2 = 0.942, RMSE=0.35) and (R 2 = 0.892, RMSE=0.41) in the testing set, respectively. Based on the machine learning model, Nb 0.22 Ta 0.22 Ti 0.24 V 0.23 W 0.09 , Nb 0.24 Ta 0.22 Ti 0.26 V 0.04 W 0.24 , Nb 0.26 Ta 0.24 Ti 0.21 V 0.24 W 0.05 , and Nb 0.18 Ta 0.26 Ti 0.22 V 0.21 W 0.13 RHEAs in the Nb-Ta-Ti-V-W RHEA system were screened and synthesized. The yield strength (1915 MPa, 1983 MPa) of the Nb 0.22 Ta 0.22 Ti 0.24 V 0.23 W 0.09 and Nb 0.24 Ta 0.22 Ti 0.26 V 0.04 W 0.24 RHEAs are higher than that (1689 MPa) of the NbTaTiVW RHEA. The unfractured Nb 0.18 Ta 0.26 Ti 0.22 V 0.21 W 0.13 and Nb 0.26 Ta 0.24 Ti 0.21 V 0.24 W 0.05 RHEAs under compression exhibit superior performance than the fracture strain (16.6 %) of the NbTaTiVW RHEA. The mixing enthalpy of RHEAs is negatively correlated with the yield strength, whereas a negative relationship exists between electronegativity difference and fracture strain through the SHAP analysis. Decreasing the mixing enthalpy and increasing the electronegativity difference promote the formation of the precipitated phase. The electron probe microanalysis reveals that the differences in mechanical properties (yield strength and fracture strain) in the NbTaTiVW RHEAs primarily stem from the fraction of the precipitated phase.

Materials & DesignVol. 246
Beijing Institute of Technology (CN), Tangshan College (CN), National Center for Nanoscience and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
High Entropy Alloys Studies
8.08
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