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It consolidates recent computational approaches and explains how simulation and data analytics support materials discovery, property prediction, and performance optimization. The analysis identifies key trends, discrepancies, and overlooked research gaps, with particular focus on steel and high entropy alloys. It also highlights methodological concentration in single-model studies and major reproducibility limitations, including frequent non-sharing of datasets and code.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/alloys-innovation-through-machine-learning-a-statistical-literature-review-290326/290326/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/alloys-innovation-through-machine-learning-a-statistical-literature-review-290326/290326.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main purpose of the review on alloys and machine learning?","Question",{"text":113,"@type":114},"The review systematically analyzes 200+ publications to clarify trends, patterns, and research gaps in using data-driven methods to accelerate alloy development.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which alloy types receive the most emphasis in the literature surveyed?",{"text":118,"@type":114},"The review reports a pronounced emphasis on steel and High Entropy Alloys.",{"name":120,"@type":111,"acceptedAnswer":121},"What reproducibility concerns does the review identify?",{"text":122,"@type":114},"It notes that 53% of papers do not share datasets and 81% do not provide access to code, underscoring limitations for verification and reuse.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290326,1789641368,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","University of Dundee  \nAlloys innovation through machine learning  \nValizadeh, Alireza; Sahara, Ryoji; Souissi, Maaouia  \nDOI:  \n10.1080/27660400.2024.2326305  \nPublication date:  \n2024  \nLicence: CC BY-NC  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nValizadeh, A. , Sahara, R. , & Souissi, M. (2024) . Alloys innovation through machine learning: a statistical literature review. Science and Technology of Advanced Materials: Methods, 4(1), Article 2326305.  \n[https://doi.org/10.1080/27660400.2024.2326305](https://doi.org/10.1080/27660400.2024.2326305)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 10. Nov. 2024  \nScience and Technology of Advanced  \nMaterials: Methods  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tstm20)[www.tandfonline.com/journals/tstm20](homepage: www.tandfonline.com/journals/tstm20)  \nAlloys innovation through machine learning: a statistical literature review  \nAlireza Valizadeh, Ryoji Sahara & Maaouia Souissi  \nTo cite this article: Alireza Valizadeh, Ryoji Sahara & Maaouia Souissi (2024) Alloys innovation through machine learning: a statistical literature review, Science and Technology of Advanced  \nMaterials: Methods, 4:1, 2326305, DOI: 10. 1080/27660400 .2024.2326305  \nTo link to this article: [https://doi.org/10.1080/27660400.2024.2326305](https://doi.org/10.1080/27660400.2024.2326305)  \n© 2024 The Author(s) . Published by National Institute for Materials Science in partnership with Taylor & Francis Group  \n\n|  Published online: 11 Apr 2024. |\n| --- |\n|  Submit your article to this journal  |\n|  Article views: 1662 |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tstm20](https://www.tandfonline.com/action/journalInformation?journalCode=tstm20)  \nAlloys innovation through machine learning: a statistical literature review  \nAlireza Valizadeh a, Ryoji Sahara b and Maaouia Souissi c,d  \naNEX Power Ltd, Milton Keynes, UK; b Research Center for Structural Materials, National Institute for Materials Science, Ibaraki, Japan; cBCAST, Brunel University London, Uxbridge, UK; dMcCombs School of Business, The University of Texas at Austin, Austin, TX, USA  \nABSTRACT  \nThis review systematically analyzes over 200 publications to explore the growing role of datadriven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. These methods harness the power of advanced simulation techniques and data analytics to expedite materials’ discovery, predict properties, and optimize performance. Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. Machine Learning techniques are widely applied across various alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably, researchers primarily investigate the physical, mechanical, and catalytic properties of materials. In terms of methodology, while 68% of the examined papers rely on a single machine learning model, the remainder employ a range of 2 to 12 models, with Neural Network being the most prevalent choice.","cbCaikgYq9xyv3yp","https://ap.wps.com/l/cbCaikgYq9xyv3yp","pdf",20702423,33,"English","# Abstract\n# Introduction\n## Machine learning adoption in alloy development\n# Impact statement\n# Article history","[{\"question\":\"What is the main purpose of the review on alloys and machine learning?\",\"answer\":\"The review systematically analyzes 200+ publications to clarify trends, patterns, and research gaps in using data-driven methods to accelerate alloy development.\"},{\"question\":\"Which alloy types receive the most emphasis in the literature surveyed?\",\"answer\":\"The review reports a pronounced emphasis on steel and High Entropy Alloys.\"},{\"question\":\"What reproducibility concerns does the review identify?\",\"answer\":\"It notes that 53% of papers do not share datasets and 81% do not provide access to code, underscoring limitations for verification and reuse.\"}]","Alloys innovation through machine learning - a statistical literature review | PDF",83]