[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118029-en":3,"doc-seo-118029-105":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":4,"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},118029,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Alloys innovation through machine learning - a statistical literature review","Systematically reviewing 200+ publications, the work examines how data-driven methods and machine learning accelerate alloy development and discovery. It consolidates recent machine-learning and computational approaches that use simulation and data analytics to predict materials properties and optimize performance. Trend and disparity analysis highlights both overlooked research gaps and the limitations of traditional trial-and-error pipelines. The review also identifies strong emphasis on steel and high-entropy alloys, while noting major reproducibility issues: most studies neither share datasets nor provide code. Results suggest the need for standardization and systematic methodology evaluation.","Science 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 (04 Mar 2024): Alloys innovation through machine learning: a statistical literature review, Science and Technology of  \nAdvanced Materials: Methods, 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|  | Accepted author version posted online: 04 Mar 2024. |\n| --- | --- |\n|  Submit your article to this journal  |  |\n|  Article views: 536 |  |\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)  \nPublisher: Taylor & Francis & The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group  \nJournal: Science and Technology of Advanced Materials: Methods  \nDOI: 10. 1080/27660400 .2024.2326305  \nAlloys Innovation through Machine Learning: A Statistical Literature Review  \nAlireza Valizadeha*, Ryoji Saharab, Maaouia Souissicd  \na NEX Power Ltd., 9 Centurion Ct, Brick Cl, Kiln Farm, Milton Keynes MK11 3JB, UK  b Research Center for Structural Materials, National Institute for Materials Science, 1-2-1  Sengen, Tsukuba, Ibaraki 305-0047, Japan   \nc BCAST, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK   \ndMcCombs School of Business, The University of Texas at Austin, Austin, TX 78705, USA *  \nCorresponding author  \nAbstract  \nThis review systematically analyzes over 200 publications to explore the growing role of data  \ndriven methods and their potential benefits in accelerating alloy development. The review  \npresents a comprehensive overview of different aspects of alloy innovation by machine learning  \nand other computational approaches used in recent years. These methods harness the power of  \nadvanced simulation techniques and data analytics to expedite materials’ discovery, predict  \nproperties, and optimize performance. Through analysis, significant trends and disparities within  \nthe data discerned, while highlighting previously overlooked research gaps, thus underscoring  \nareas that require further exploration. Machine Learning techniques are widely applied across  \nvarious alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably,  \nresearchers primarily investigate the physical, mechanical, and catalytic properties of materials.  \nIn terms of methodology, while 68% of the examined papers rely on a single machine learning  \nmodel, the remainder employ a range of 2 to 12 models, with Neural Network being the most  \nprevalent choice. However, a notable concern arises as 53% of these papers do not share their  \ndataset, and a staggering 81% do not provide access to their code. Paramount importance of adopting a systematic approach when scrutinizing machine learning methodologies is underscored. Analysis shows lack of consistency and diversity in the methods employed by researchers in the field of alloy development, highlighting the potential for improvement through  \nstandardization. The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm.  \nKeywords: Alloy development, machine learning, data-driven research, materials  \ninformatics, Materials Genome I","cbCaiig37O0mTdSS","https://ap.wps.com/l/cbCaiig37O0mTdSS","pdf",7126546,1,68,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What is the main goal of the statistical literature review?\",\"answer\":\"The review systematically analyzes 200+ publications to clarify the growing role of data-driven methods and machine learning in accelerating alloy development and discovery.\"},{\"question\":\"Which types of alloys and properties receive the most focus?\",\"answer\":\"Machine learning is applied across many alloys, with a pronounced emphasis on steel and high-entropy alloys. Research commonly targets physical, mechanical, and catalytic properties.\"},{\"question\":\"What reproducibility and reporting concerns does the review identify?\",\"answer\":\"A major concern is that most papers do not share datasets and most do not provide access to code, reducing reproducibility. The findings motivate a more systematic and standardized methodological approach.\"}]","Alloys innovation through machine learning - a statistical literature review | PDF",1785680853,171,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"alloys-innovation-through-machine-learning-a-statistical-literature-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/alloys-innovation-through-machine-learning-a-statistical-literature-review/118029/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the statistical literature review?","Question",{"text":75,"@type":76},"The review systematically analyzes 200+ publications to clarify the growing role of data-driven methods and machine learning in accelerating alloy development and discovery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which types of alloys and properties receive the most focus?",{"text":80,"@type":76},"Machine learning is applied across many alloys, with a pronounced emphasis on steel and high-entropy alloys. Research commonly targets physical, mechanical, and catalytic properties.",{"name":82,"@type":73,"acceptedAnswer":83},"What reproducibility and reporting concerns does the review identify?",{"text":84,"@type":76},"A major concern is that most papers do not share datasets and most do not provide access to code, reducing reproducibility. 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