Machine learning informed tetragonal ratio c/a of martensite-Reference-Cited by-同舟云学术

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Machine learning informed tetragonal ratio c/a of martensite

Published:2024-01 Issue: Volume:233 Page:112735
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Liu Hao-Xuan,Yan Hai-Le^ORCID,Zhao Ying,Jia Nan,Tang Shuai,Cong Daoyong,Yang Bo,Li Zongbin,Zhang Yudong,Esling Claude,Zhao Xiang,Zuo Liang

Funder

Fundamental Research Funds for the Central Universities

Higher Education Discipline Innovation Project

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference66 articles.

1. Evolution of precipitated phases during prolonged tempering in a 9%Cr1%MoVNb ferritic–martensitic steel: Influence on creep performance;Zavaleta Gutiérrez;Mater. Sci. Eng. A,2011

2. Unprecedented non-hysteretic superelasticity of 001-oriented NiCoFeGa single crystals;Chen;Nat. Mater.,2020

3. 12% magnetic field-induced strain in Ni-Mn-Ga-based non-modulated martensite;Sozinov;Appl. Phys. Lett.,2013

4. The effect of pressure on the magnetic and structural transition temperatures of the shape memory alloys Ni50+x(Mn0.5Fe0.5)25Ga25-x, Physica B Condens;Adachi;Matter,2015

5. Simultaneously achieved large reversible elastocaloric and magnetocaloric effects and their coupling in a magnetic shape memory alloy;Qu;Acta Mater.,2018

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