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Modeling solid solution strengthening in high entropy alloys using machine learning
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DOI：10.1016/j.actamat.2021.116917.png)
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Abstract
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Solid solution strengthening (SSS) influences the exceptional mechanical properties of single-phase high entropy alloys (HEAs). Thus, given the vast compositional space, identifying the underlying factors that control SSS to accelerate property-oriented design of HEAs is an outstanding challenge. In the present work, we demonstrate a relationship derived in terms of the electronegative difference of elements to characterize SSS for HEAs. We propose a model which shows superior performance in predicting solid solution strength/hardness of HEAs compared to existing physics-based models. We discuss applications of our SSS model to HEA design and predict alloys with potentially high SSS in the four alloy systems AlCoCrFeNi, CoCrFeNiMn, HfNbTaTiZr and MoNbTaWV. Our findings are based on the use of machine learning (ML) methods involving feature construction and feature selection, which we employ to capture salient descriptors. (c) 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Keywords:
High entropy alloys
Solid solution strengthening
Machine learning
Alloy design
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