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First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation

Crossref DOI link: https://doi.org/10.1016/j.actamat.2019.09.026

Published Print: 2019-12

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Kim, George

Diao, Haoyan

Lee, Chanho

Samaei, A.T.

Phan, Tu

de Jong, Maarten

An, Ke https://orcid.org/0000-0002-6093-429X
Ma, Dong https://orcid.org/0000-0003-3154-2454
Liaw, Peter K. https://orcid.org/0000-0002-3054-5955
Chen, Wei https://orcid.org/0000-0002-1135-7721
Funding

Funding for this research was provided by:

Army Research Office

U.S. Department of Energy

Office of Science

National Science Foundation
License Information

Text and Data Mining valid from 2019-12-01

Text and Data Mining valid from 2019-12-01

Accepted Manuscript valid from 2020-10-10
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This article is maintained by: Elsevier

Article Title: First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation

Journal Title: Acta Materialia

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.actamat.2019.09.026

Content Type: article

Copyright: © 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.