Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach-Reference-Cited by-同舟云学术

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Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach

Published:2024-04 Issue: Volume:239 Page:112976
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Chen Cun,Han Xiaoli,Zhang Yong^ORCID,Liaw Peter K.,Ren Jingli

Funder

Army Research Office

National Natural Science Foundation of China

National Science Foundation

Publisher

Elsevier BV

Reference67 articles.

1. Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes;Yeh;Adv. Eng. Mater.,2004

2. Effect of elemental interaction on microstructure of CuCrFeNiMn high entropy alloy system;Ren;J. Alloys Compd.,2010

3. Machine learning assisted design of high entropy alloys with desired property;Wen;Acta Mater.,2019

4. Mechanical, corrosion, and wear properties of biomedical Ti-Zr-Nb-Ta-Mo high entropy alloys;Hua;J. Alloys Compd.,2021

5. Modeling solid solution strengthening in high entropy alloys using machine learning;Wen;Acta Mater.,2021

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1. A Modern Approach to HEAs: From Structure to Properties and Potential Applications;Crystals;2024-05-09

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