Data-driven prediction of grain boundary segregation and disordering in high-entropy alloys in a 5D space-Reference-Cited by-同舟云学术

Data-driven prediction of grain boundary segregation and disordering in high-entropy alloys in a 5D space

Published:2022 Issue: Volume: Page:
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Hu Chongze¹²^ORCID,Luo Jian¹²^ORCID

Affiliation:

1. Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, USA

2. Program of Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, USA

Abstract

A data-driven approach can predict the properties of grain boundaries in high-entropy alloys as functions of four independent compositional degrees of freedom and temperature in a 5D space and reveal new interfacial phenomena.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2022/MH/D1MH01204E

Reference58 articles.

1. High-entropy alloy: challenges and prospects

2. High-Entropy Alloys: A Critical Review

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

4. High-entropy alloys

5. Complexion: A new concept for kinetic engineering in materials science

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