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High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.ORCID iD: 0000-0002-7527-719X
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
2022 (English)In: Frontiers in Materials, ISSN 2296-8016, Vol. 9, article id 889771Article, review/survey (Refereed) Published
Abstract [en]

Introduction of high entropy alloys or multi-principal element alloys around 15 years ago motivated revising conventional alloy design strategies and proposed new ways for alloy development. Despite significant research since then, the potential for new material discoveries using the MPEA concept has hardly been scratched. Given the number of available elements and the vastness of possible composition combinations, an unlimited number of alloys are waiting to be investigated! Discovering novel high-performance materials can be like finding a needle in a haystack, which demands an enormous amount of time and computational capacity. To overcome the challenge, a systematic approach is essential to meet the growing demand for developing novel high-performance or multifunctional materials. This article aims to briefly review the challenges, recent progress and gaps, and future outlook in accelerated alloy development, with a specific focus on computational high-throughput (HT) screening methods integrated with the Calculation of Phase Diagrams (CALPHAD) technique. Copyright © 2022 Ghassemali and Conway.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022. Vol. 9, article id 889771
Keywords [en]
alloy design, high entropy alloy, high-throughput approaches, machine learning, phase diagram, Alloying, Entropy, Alloy designs, Alloy development, Calculation of phase diagrams, Computational capacity, Conventional alloys, Design strategies, High entropy alloys, High performance material, High-throughput, Phase diagrams
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:hj:diva-56338DOI: 10.3389/fmats.2022.889771ISI: 000806274200001Scopus ID: 2-s2.0-85128464176Local ID: GOA;;809023OAI: oai:DiVA.org:hj-56338DiVA, id: diva2:1655424
Funder
Vinnova, 2018-02379, 2021-01918Knowledge Foundation, 20200216Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2022-06-23Bibliographically approved

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Ghassemali, EhsanConway, Patrick L. J.

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