[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116836-en":3,"doc-seo-116836-105":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},116836,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Exploiting Machine Learning in Multiscale Modelling of Materials","Recent advances in efficient machine learning algorithms have accelerated interest in materials science, where intrinsically complex multiscale problems pose major computational and modeling challenges. This article addresses the gap in which algorithm development and practical multiscale materials applications remain disconnected, with many methods used as black-box models. It provides an overview of machine learning approaches for equivariant properties, machine learning-aided statistical mechanics, integration of ab initio components, and uncertainty quantification.","Manuscript version: Published Version  \nThe version presented in WRAP is the published version (Version of Record) .  \nPersistent WRAP URL:  \n[http://wrap.warwick.ac.uk/171506](http://wrap.warwick.ac.uk/171506)  \n[How to cite:](How to cite:)  \nThe repository item page linked to above, will contain details on accessing citation guidance from the publisher.  \nCopyright and reuse:  \nThe Warwick Research Archive Portal (WRAP) makes this work of researchers of the University of Warwick available open access under the following conditions.  \nThis article is made available under the Creative Commons Attribution 4.0 International license (CC BY 4 .0) and may be reused according to the conditions of the license. For more details see: [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) .  \nPublisher’s statement:  \nPlease refer to the repository item page, publisher’s statement section, for further information.  \nFor more information, please contact [the WRAP Team at: wrap@warwick.ac.uk](the WRAP Team at: wrap@warwick.ac.uk)  \nJ. Inst. Eng. India Ser. D [https://doi.org/10.1007/s40033-022-00424-z](https://doi.org/10.1007/s40033-022-00424-z)  \nARTICLE OF PROFESSIONAL INTERESTS  \nExploiting Machine Learning in Multiscale Modelling of Materials  \nG. Anand1 · Swarnava Ghosh2 · Liwei Zhang3 · Angesh Anupam4 ·  \nColin L. Freeman5 · Christoph Ortner3 · Markus Eisenbach2 · James R. Kermode6  \nReceived: 10 September 2022 / Accepted: 28 October 2022 © The Institution of Engineers (India) 2022  \nAbstract Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering posea formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘blackbox’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview  \nThis manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan ([http://energy.gov/downloads/doe-public](http://energy.gov/downloads/doe-public)access-plan) .  \n* G. Anand [ganand@metal.iiests.ac.in](ganand@metal.iiests.ac.in)  \n1 Department of Metallurgy and Materials Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India  \n2 National Centre for Computational Sciences, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37831, USA  \n3 Department of Mathematics, University of British Columbia, 1984 Mathematics Rd, Vancouver, BC V6T1Z2, Canada  \n4 Department of Computer Science, Cardiff Metropolitan University, Llandaff Campus, Cardiff, Wales CF5 2YB, UK  \n5 Department of Materials Science and Engineering, University of Sheffield, Mappin St, Sheffield S1 3JD, UK  \n6 Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Warwick CV4 7AL, UK  \nof machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.  \nIntroduction  \nThere has been extensive recent interest in the application of machine learning in diverse fields within materials science and engineering. Numerous review papers [1–6], viewpoints [7, 8]","cbCaicH6ZLkHQI0b","https://ap.wps.com/l/cbCaicH6ZLkHQI0b","pdf",1804696,1,12,"English","en",105,"# Introduction\n## Multiscale simulation challenges and the role of ML\n## Two main ML directions in multiscale materials modelling\n## Machine learning models: motivation, application, and challenges","[{\"question\":\"What motivation does the article give for using machine learning in multiscale materials modelling?\",\"answer\":\"The article links rapid progress in efficient ML algorithms with the need to tackle complex multiscale problems in materials science and engineering, where conventional multiscale simulation can introduce errors through extrapolation.\"},{\"question\":\"What problem does the article identify in current ML research within materials science?\",\"answer\":\"It highlights a clear gap: efficient algorithms are often developed separately, then applied by others as black-box models rather than being tightly integrated into multiscale modelling workflows.\"},{\"question\":\"Which ML topics and methods does the article plan to cover for multiscale modelling?\",\"answer\":\"It discusses learning of equivariant properties, ML-aided statistical mechanics, incorporation of ab initio approaches in multiscale models of materials processing, application of ML to uncertainty quantification, and the use of Bayesian approaches for multiscale modelling.\"}]","Exploiting Machine Learning in Multiscale Modelling of Materials | PDF",1785671999,30,{"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},"exploiting-machine-learning-in-multiscale-modelling-of-materials","",{"@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/exploiting-machine-learning-in-multiscale-modelling-of-materials/116836/",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 motivation does the article give for using machine learning in multiscale materials modelling?","Question",{"text":75,"@type":76},"The article links rapid progress in efficient ML algorithms with the need to tackle complex multiscale problems in materials science and engineering, where conventional multiscale simulation can introduce errors through extrapolation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the article identify in current ML research within materials science?",{"text":80,"@type":76},"It highlights a clear gap: efficient algorithms are often developed separately, then applied by others as black-box models rather than being tightly integrated into multiscale modelling workflows.",{"name":82,"@type":73,"acceptedAnswer":83},"Which ML topics and methods does the article plan to cover for multiscale modelling?",{"text":84,"@type":76},"It discusses learning of equivariant properties, ML-aided statistical mechanics, incorporation of ab initio approaches in multiscale models of materials processing, application of ML to uncertainty quantification, and the use of Bayesian approaches for multiscale modelling.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]