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

Vazquez, Guillermo; Singh, Prashant; Sauceda, Daniel; Couperthwaite, Richard; Britt, Nicholas; Youssef, Khaled; Johnson, Duane D.; Arróyave, Raymundo

doi:10.1016/j.actamat.2022.117924

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

Journal Article · Thu Apr 14 00:00:00 EDT 2022 · Acta Materialia

DOI:https://doi.org/10.1016/j.actamat.2022.117924· OSTI ID:1890191

^[1]; ^[2]; Sauceda, Daniel ^[1]; ^[1]; ^[3]; Youssef, Khaled ^[3]; ^[4]; Arróyave, Raymundo ^[1]

Texas A & M Univ., College Station, TX (United States)
Texas A & M Univ., College Station, TX (United States); Qatar University, Doha (Qatar)
Qatar University, Doha (Qatar)
Qatar University, Doha (Qatar); Iowa State Univ., Ames, IA (United States)

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. Here 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.

View Accepted Manuscript (DOE)

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS); National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-07CH11358; AR0001427

OSTI ID:: 1890191

Alternate ID(s):: OSTI ID: 1894256
OSTI ID: 2202742

Report Number(s):: IS-J 10,903

Journal Information:: Acta Materialia, Journal Name: Acta Materialia Vol. 232; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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