High-throughput data-driven machine learning prediction of thermal expansion coefficients of high-entropy solid solution carbides-Reference-Cited by-同舟云学术

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High-throughput data-driven machine learning prediction of thermal expansion coefficients of high-entropy solid solution carbides

Published:2024-08 Issue: Volume:122 Page:106738
ISSN:0263-4368
Container-title:International Journal of Refractory Metals and Hard Materials
language:en
Short-container-title:International Journal of Refractory Metals and Hard Materials

Author:

Kim Myungjae,Kim Jiho,Kim Hyokyeong,Kim Jiwoong

Funder

Korea Ministry of Trade Industry and Energy

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

National Research Council of Science and Technology

Korea Ministry of Science and ICT

Publisher

Elsevier BV

Reference43 articles.

1. High-entropy carbide: a novel class of multicomponent ceramics;Zhou;Ceram. Int.,2018

2. High-entropy ceramics;Oses;Nat. Rev. Mater.,2020

3. Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide;Pak;Npj Comput. Mater.,2023

4. Synthesis and characterization of multicomponent (CrNbTaTiW)C films for increased hardness and corrosion resistance;Malinovskis;Mater. Des.,2018

5. Single-phase (Hf-Mo-Nb-ta-Ti)C high-entropy ceramic: a potential high temperature anti-wear material;Sun;Tribol. Int.,2021

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