Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

[Display omitted] We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Indep...

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Bibliographic Details
Published in:Acta materialia 2022-06, Vol.232, p.117924, Article 117924
Main Authors: Vazquez, Guillermo, Singh, Prashant, Sauceda, Daniel, Couperthwaite, Richard, Britt, Nicholas, Youssef, Khaled, Johnson, Duane D., Arróyave, Raymundo
Format: Article
Language:English
Subjects:
Density-functional theory
Descriptors
Elastic properties
Machine learning
refractory high entrophy alloys
Refractory high entropy alloys
SISSO
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Summary:[Display omitted] We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. We also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.
ISSN:1359-6454
1873-2453
DOI:10.1016/j.actamat.2022.117924