Berry, J. orcid.org/0000-0001-7291-2306 and Christofidou, K.A. orcid.org/0000-0002-8064-5874 (2024) Supervised machine learning for multi-principal element alloy structural design. Materials Science and Technology. ISSN 0267-0836
Abstract
The application of supervised Machine Learning (ML) in material science, especially towards the design of structural Multi-Principal Element Alloys (MPEAs) has rapidly accelerated over the past five years. However, several factors are limiting the impact that these ML methodologies can have, chief amongst them being the availability and fidelity of data. This review analyses how ML has been utilised to accelerate the design of novel structural MPEAs, outlining the standard procedures followed, and highlighting the successes and common pitfalls identified in current studies. The need for experimental validation and incorporation into closed loop ML pipelines is also discussed, including the influence and integration of manufacturing methodologies into the ML decision making process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | multi-principal element alloys; highen tropy alloys; complex concentrated alloys; compositionally complex alloys; machine learning; experimental data; alloy design |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/S022635/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Oct 2024 10:30 |
Last Modified: | 11 Oct 2024 10:30 |
Published Version: | http://dx.doi.org/10.1177/02670836241272086 |
Status: | Published online |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/02670836241272086 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218181 |