[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121026-en":3,"doc-seo-121026-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121026,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Supervised machine learning for multi-principal element alloy structural design","The application of supervised machine learning in materials science, especially for designing structural Multi-Principal Element Alloys (MPEAs), has grown rapidly over the past five years. Constraints on data availability and fidelity limit the real-world impact of current ML workflows. This review examines how ML is used to accelerate novel structural MPEA design, summarizes typical procedures, and discusses observed successes and recurring pitfalls. It also covers the role of experimental validation and integration into closed-loop ML pipelines, including how manufacturing steps influence ML decision-making.","This is a repository copy of Supervised machine learning for multi-principal element alloy structural design.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/218181/](https://eprints.whiterose.ac.uk/218181/)  \nVersion: Published Version  \nArticle:  \nBerry, [J. orcid.org/0000-0001-7291-2306 and Christofidou](J. orcid.org/0000-0001-7291-2306 and Christofidou) , [K.A. orcid.org/0000-0002-8064-](K.A. orcid.org/0000-0002-8064-)[ ](K.A. orcid.org/0000-0002-8064-)[5874](5874) (2024) Supervised machine learning for multi-principal element alloy structural design. Materials Science and Technology. ISSN 0267-0836  \n[https://doi.org/10.1177/02670836241272086](https://doi.org/10.1177/02670836241272086)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nMST Literature Review Prize 202  \nSupervised machine learning for multiprincipal element alloy structural design  \nJoshua Berry 1  and Katerina A. Christofidou1   \nMaterials Science and Technology 1–18  \n© The Author(s) 2024  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/02670836241272086](DOI: 10.1177/02670836241272086)[ ](DOI: 10.1177/02670836241272086)[journals.sagepub.com/home/mst](journals.sagepub.com/home/mst)  \nAbstract  \nThe application of supervised Machine Learning (ML) in material science, especially towards the design of structural MultiPrincipal 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.  \nKeywords  \nmulti-principal element alloys, highen tropy alloys, complex concentrated alloys, compositionally complex alloys, machine learning, experimental data, alloy design  \nReceived: 2 February 2024; accepted: 2 July 2024  \nIntroduction  \nHigh Entropy Alloys (HEAs), first introduced to the scientific community in 2004 by Yeh et al.1 and Cantor et al.2 respectively, are conventionally defined as a class of alloys containing five or more elements in either equiatomic elemental concentrations, or elemental concentrations in the range of 5 to 35 at.% .1 This concept leads to HEAs occupying a vast uncharted compositional space3 and sparking a wealth of studies and debates in the literature, not least on appropriate naming conventions. Consequently, several different terms have been proposed and are used to encompass different classes of materials such as, multi-component alloys, compositionally complex alloys, complex concentrated alloys or indeed the broader term, Multi-Principal Element Alloys (MPEAs) . Concurrently, the term HEA has evolved to more routinely describe single phase MPEAs.4,5 For consistency in this review, MPEA w","cbCaigauPKNSvbza","https://ap.wps.com/l/cbCaigauPKNSvbza","pdf",1954959,1,19,"English","en",105,"# Introduction\n## High entropy alloys and MPEA framing\n## Supervised machine learning basics\n# Review scope and goals\n## Standard ML procedures in MPEA design\n## Successes and common pitfalls\n## Experimental validation and closed-loop integration","[{\"question\":\"Why is data availability and fidelity critical for supervised ML in structural MPEA design?\",\"answer\":\"The review highlights that limited access to high-quality data and uncertainty in data fidelity constrain how effectively ML methods can influence design impact.\"},{\"question\":\"What supervised learning tasks are discussed for materials design?\",\"answer\":\"Supervised learning is described as being trained with known target outputs and subdivided into classification tasks for discrete categories and regression tasks for continuous numerical predictions.\"},{\"question\":\"How does the review address integrating ML with experimental validation and manufacturing?\",\"answer\":\"It emphasizes the need for experimental validation and incorporation into closed-loop ML pipelines, including considering how manufacturing methodologies and post-processing affect ML decision-making.\"}]","Supervised machine learning for multi-principal element alloy structural design | 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