Machine learning guided phase formation prediction of high entropy alloys-Reference-Cited by-同舟云学术

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Machine learning guided phase formation prediction of high entropy alloys

Published:2022-08 Issue: Volume:32 Page:104146
ISSN:2352-4928
Container-title:Materials Today Communications
language:en
Short-container-title:Materials Today Communications

Author:

Qu Nan,Liu Yong,Zhang Yan,Yang Danni,Han Tianyi,Liao Mingqing,Lai Zhonghong,Zhu Jingchuan,Zhang Lin

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Materials Chemistry,Mechanics of Materials,General Materials Science

Reference38 articles.

1. Tuning element distribution, structure and properties by composition in high-entropy alloys;Ding;Nature,2019

2. Enhanced strength and ductility in a high-entropy alloy via ordered oxygen complexes;Lei;Nature,2018

3. Short-range order and its impact on the CrCoNi medium-entropy alloy;Zhang;Nature,2020

4. High-entropy alloys;George;Nat. Rev. Mater.,2019

5. Microstructure evolution and critical stress for twinning in the CrMnFeCoNi high-entropy alloy;Laplanche;Acta Mater.,2016

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2. Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach;Computational Materials Science;2024-04

3. Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature;International Journal of Minerals, Metallurgy and Materials;2024-04

4. Comprehensive insights into recent innovations: Magnesium-inclusive high-entropy alloys;Journal of Magnesium and Alloys;2024-04

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