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Machine learning strategies for high-entropy alloys

Published:2020-12-14 Issue:22 Volume:128 Page:221101
ISSN:0021-8979
Container-title:Journal of Applied Physics
language:en
Short-container-title:Journal of Applied Physics

Author:

Rickman J. M.¹²^ORCID,Balasubramanian G.³,Marvel C. J.²,Chan H. M.²^ORCID,Burton M.-T.²

Affiliation:

1. Department of Physics, Lehigh University, Bethlehem, Pennsylvania 18015, USA

2. Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA

3. Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, Pennsylvania 18015, USA

Funder

National Science Foundation

Office of Naval Research Global

Publisher

AIP Publishing

Subject

General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0030367

Reference77 articles.

1. A critical review of high entropy alloys and related concepts

2. High-entropy alloys: a critical assessment of their founding principles and future prospects

3. High-Entropy Alloys: A Critical Review

4. From high-entropy alloys to complex concentrated alloys

5. Microstructural development in equiatomic multicomponent alloys

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4. Machine learning-enabled prediction and optimization of hardness for Nb-Ti-V-Zr refractory high entropy alloy;Materials Today Communications;2024-08

5. Prediction and Rational Design of Stacking Fault Energy of Austenitic Alloys Based on Interpretable Machine Learning and Chemical Composition;steel research international;2024-06-05

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