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AuthorGuillermo, Vazquez
AuthorSingh, Prashant
AuthorSauceda, Daniel
AuthorCouperthwaite, Richard
AuthorBritt, Nicholas
AuthorYoussef, Khaled
AuthorJohnson, Duane D.
AuthorArróyave, Raymundo
Available date2022-05-10T08:55:23Z
Publication Date2022-06-15
Publication NameActa Materialia
Identifierhttp://dx.doi.org/10.1016/j.actamat.2022.117924
CitationVazquez, G., Singh, P., Sauceda, D., Couperthwaite, R., Britt, N., Youssef, K., ... & Arróyave, R. (2022). Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys. Acta Materialia, 117924.
ISSN13596454
URIhttps://www.sciencedirect.com/science/article/pii/S1359645422003068
URIhttp://hdl.handle.net/10576/30803
AbstractWe 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.
Languageen
PublisherElsevier
SubjectRefractory high entropy alloys
Elastic properties
Machine learning
Descriptors
SISSO
Density-functional theory
TitleEfficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
TypeArticle
Volume Number232
dc.accessType Open Access


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