Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data-Reference-Cited by-同舟云学术

Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

Published:2024-08 Issue: Volume:493 Page:152606
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Dangwal Shivam^ORCID,Ikeda Yuji^ORCID,Grabowski Blazej,Edalati Kaveh^ORCID

Funder

Japan Society for the Promotion of Science

Ministry of Education, Culture, Sports, Science and Technology

Horizon 2020

Deutsche Forschungsgemeinschaft

European Research Council

Baden-Württemberg Stiftung

Horizon 2020 Framework Programme

Publisher

Elsevier BV

Reference135 articles.

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4. Materials for hydrogen-based energy storage – past, recent progress and future outlook;Hirscher;J. Alloys Compd.,2020

5. Reversible room temperature hydrogen storage in high-entropy alloy TiZrCrMnFeNi;Edalati;Scr. Mater.,2020

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3. A review on 2D materials: unveiling next-generation hydrogen storage solutions, advancements and prospects;Journal of Materials Science;2024-08-13