[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119216-en":3,"doc-seo-119216-105":30,"detail-sidebar-cat-0-en-105":92},{"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},119216,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning-driven atomistic analysis of mechanical behavior in silicon nanowires - Accepted Version","Machine learning-driven atomistic analysis is applied to characterize mechanical behavior in silicon nanowires. The study builds upon atomistic simulations and integrates machine learning models to accelerate insight into stress–strain response and underlying material features. Cross-comparison between model predictions and simulation-based results is used to validate generalization across conditions. Reported outcomes emphasize improved efficiency without sacrificing accuracy, supporting the extraction of mechanical trends from large-scale atomistic datasets and enabling more practical modeling workflows for nanowire mechanics.","This is a repository copy of Machine learning-driven atomistic analysis of mechanical behavior in silicon nanowires.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/221302/](https://eprints.whiterose.ac.uk/221302/)  \n[Version: Accepted Version](Version: Accepted Version)  \nArticle:  \nZare Pakzad, Sina, Nasr Esfahani, Mohammad [orcid.org/0000-0002-6973-2205](orcid.org/0000-0002-6973-2205) , Canadinc, Demircan et al. (1 more author) (2025) Machine learning-driven atomistic analysis of mechanical behavior in silicon nanowires. Computational Materials Science.  \n113446. ISSN 0927-0256  \n[https://doi.org/10.1016/j.commatsci.2024.113446](https://doi.org/10.1016/j.commatsci.2024.113446)  \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/)  \n▼❛❝❤✐♥❡ ▲❡❛r♥✐♥❣✲❉r✐✈❡♥ ❆t♦♠✐st✐❝ ❆♥❛❧②s✐s ♦❢▼❡❝❤❛♥✐❝❛❧ ❇❡❤❛✈✐♦r ✐♥ ❙✐❧✐❝♦♥ ◆❛♥♦✇✐r❡s  \n❙✐♥❛ ❩❛r❡ P❛❦③❛❞❛ ✱ ▼♦❤❛♠♠❛❞ ◆❛sr ❊s❢❛❤❛♥✐❜ ✱ ❉❡♠✐r❝❛♥ ❈❛♥❛❞✐♥❝❝ ✱ ❇✳  \n❊r❞❡♠ ❆❧❛❝❛❛✱❞✱❡✱∗  \n❛ ❉❡♣❛rt♠❡♥t ♦❢ ▼❡❝❤❛♥✐❝❛❧ ❊♥❣✐♥❡❡r✐♥❣✱ ❑♦ç ❯♥✐✈❡rs✐t②✱ ❘✉♠❡❧✐❢❡♥❡r✐ ❨♦❧✉✱ ✸✹✹✺✵  \n❙❛r✐②❡r✱ ■st❛♥❜✉❧✱ ❚✉r❦❡②  \n❜ ❙❝❤♦♦❧ ♦❢ P❤②s✐❝s✱ ❊♥❣✐♥❡❡r✐♥❣ ❛♥❞ ❚❡❝❤♥♦❧♦❣②✱ ❯♥✐✈❡rs✐t② ♦❢ ❨♦r❦✱ ❨♦r❦✱ ❨❖✶✵ ✺❉❉✱  \n❯❑  \n❝ ❆❞✈❛♥❝❡❞ ▼❛t❡r✐❛❧s ●r♦✉♣ ✭❆▼●✮✱ ❉❡♣❛rt♠❡♥t ♦❢ ▼❡❝❤❛♥✐❝❛❧ ❊♥❣✐♥❡❡r✐♥❣✱ ❑♦ç❯♥✐✈❡rs✐t②✱ ✸✹✹✺✵✱ ■st❛♥❜✉❧✱ ❚✉r❦❡②  \n❞ ♥2 ❙❚❆❘✲❑♦ç ❯♥✐✈❡rs✐t② ◆❛♥♦❢❛❜r✐❝❛t✐♦♥ ❛♥❞ ◆❛♥♦❝❤❛r❛❝t❡r✐③❛t✐♦♥ ❈❡♥t❡r ❢♦r ❙❝✐❡♥t✐✜❝ ❛♥❞ ❚❡❝❤♥♦❧♦❣✐❝❛❧ ❆❞✈❛♥❝❡❞ ❘❡s❡❛r❝❤✱ ❑♦ç ❯♥✐✈❡rs✐t②✱ ❘✉♠❡❧✐❢❡♥❡r✐ ❨♦❧✉✱  \n✸✹✹✺✵ ❙❛r✐②❡r✱ ■st❛♥❜✉❧✱ ❚✉r❦❡②  \n❡ ❑♦ç ❯♥✐✈❡rs✐t② ❙✉r❢❛❝❡ ❚❡❝❤♥♦❧♦❣✐❡s ❘❡s❡❛r❝❤ ❈❡♥t❡r ✭❑❯❨❚❆▼✮✱ ❑♦ç ❯♥✐✈❡rs✐t②✱  \n✸✹✹✺✵ ❙❛r✐②❡r✱ ■st❛♥❜✉❧✱ ❚✉r❦❡②  \n❆❜str❛❝t  \n❚❤✐s st✉❞② ✐♥✈❡st✐❣❛t❡s t❤❡ ♠♦❞✉❧✉s ♦❢ ❡❧❛st✐❝✐t② ♦❢ s✐❧✐❝♦♥ ♥❛♥♦✇✐r❡s ✉s✲✐♥❣ ❛ ❝♦♠❜✐♥❛t✐♦♥ ♦❢ ♠♦❧❡❝✉❧❛r ❞②♥❛♠✐❝s s✐♠✉❧❛t✐♦♥s ❛♥❞ ♠❛❝❤✐♥❡ ❧❡❛r♥✐♥❣ t❡❝❤♥✐q✉❡s✳ ❚❤❡ r❡s❡❛r❝❤ ♣r❡s❡♥ts ❛ s✉❜st❛♥t✐❛❧ ❞❛t❛s❡t ✇✐t❤ ♦✈❡r ✸✵✵✵ ❞❛t❛♣♦✐♥ts ♦❜t❛✐♥❡❞ ❢r♦♠ ♠♦❧❡❝✉❧❛r ❞②♥❛♠✐❝s s✐♠✉❧❛t✐♦♥s✱ ✇❤✐❝❤ r❡✈❡❛❧s ❞❡t❛✐❧❡❞✐♥s✐❣❤ts ✐♥t♦ t❤❡ ♠❡❝❤❛♥✐❝❛❧ ♣r♦♣❡rt✐❡s ♦❢ s✐❧✐❝♦♥ ♥❛♥♦✇✐r❡s ❛♥❞ ✉♥❞❡rs❝♦r❡st❤❡ ✐♠♣♦rt❛♥❝❡ ♦❢ ❛❝❝✉r❛t❡ ♠♦❞❡❧ ❝❛❧✐❜r❛t✐♦♥✳ ▼❛❝❤✐♥❡ ❧❡❛r♥✐♥❣ s✉rr♦❣❛t❡♠♦❞❡❧s ❛r❡ ❡♠♣❧♦②❡❞ t♦ ♣r❡❞✐❝t t❤❡ ❡❧❛st✐❝✐t② ♦❢ s✐❧✐❝♦♥ ♥❛♥♦✇✐r❡s✱ ❢♦❝✉s✐♥❣♦♥ t❤❡ ✐♥✢✉❡♥❝❡ ♦❢ s✉r❢❛❝❡ st❛t❡ ❛♥❞ ❝r②st❛❧ ♦r✐❡♥t❛t✐♦♥✳ ❇② ❛♥❛❧②③✐♥❣ ♣❛r✲ t✐❛❧ ❞❡♣❡♥❞❡♥❝✐❡s ❛♥❞ ✉s✐♥❣ ✐♥✈❡rs❡ ♣♦❧❡ ✜❣✉r❡s✱ t❤❡ st✉❞② ❞❡♠♦♥str❛t❡s t❤❛tt❤❡ ♠♦❞✉❧✉s ♦❢ ❡❧❛st✐❝✐t② ❡①❤✐❜✐ts s✐❣♥✐✜❝❛♥t ♦r✐❡♥t❛t✐♦♥ ❞❡♣❡♥❞❡♥❝❡✳ ❚❤✐s ✇♦r❦ ❜r✐❞❣❡s ❝♦♠♣✉t❛t✐♦♥❛❧ ❛♥❞ ❡①♣❡r✐♠❡♥t❛❧ ❛♣♣r♦❛❝❤❡s✱ ♦✛❡r✐♥❣ ❛ r❡✜♥❡❞✉♥❞❡rst❛♥❞✐♥❣ ♦❢ t❤❡ ♠❡❝❤❛♥✐❝❛❧ ❜❡❤❛✈✐♦r ♦❢ s✐❧✐❝♦♥ ♥❛♥♦✇✐r❡s✳ ❚❤❡ ✜♥❞✐♥❣s ❤✐❣❤❧✐❣❤t t❤❡ ♣♦t❡♥t✐❛❧ ♦❢ ✐♥t❡❣r❛t✐♥❣ ♠❛❝❤✐♥❡ ❧❡❛r♥✐♥❣ ✇✐t❤ ❛t♦♠✐st✐❝ s✐♠✲✉❧❛t✐♦♥s t♦ ✐♠♣r♦✈❡ t❤❡ ♣r❡❞✐❝t✐✈❡ ❛❝❝✉r❛❝② ♦❢ ♠❛t❡r✐❛❧ ♣r♦♣❡rt✐❡s✱ ❜✉✐❧❞✐♥❣ t❤❡ ❢r❛♠❡✇♦r❦ ❢♦r ❛❞✈❛♥❝❡♠❡♥ts ✐♥ ♥❛♥♦❡❧❡❝tr♦♠❡❝❤❛♥✐❝❛❧ ❛♣♣❧✐❝❛t✐♦♥s✳  \n❑❡②✇♦r❞s✿ ❙✐❧✐❝♦♥ ♥❛♥♦✇✐r❡✱ ♠♦❧❡❝✉❧❛r ❞②♥❛♠✐❝s✱ ♠❛❝❤✐♥❡ ❧❡❛r♥✐♥❣✱ t❡♥s✐❧❡❜❡❤❛✈✐♦r✱ ♠♦❞✉❧✉s ♦❢ ❡❧❛st✐❝✐t②  \n✶  \n✶✳ ■♥tr♦❞✉❝t✐♦♥  \n◆❛♥♦✇✐r❡s ✭◆❲s✮ ❡①❤✐❜✐t ❛ s✐❣♥✐✜❝❛♥t ♣♦t❡♥t✐❛❧ ❛s ❡ss❡♥t✐❛❧ ❜✉✐❧❞✐♥❣ ❜❧♦❝❦s ❢♦r t❤❡ ❞❡✈❡❧♦♣♠❡♥t ♦❢ ♥❛♥♦❡❧❡❝tr♦♠❡❝❤❛♥✐❝❛❧ s②st❡♠s ✭◆❊▼❙✮ ❬✶❪✱ ♥❛♥♦❡❧❡❝✲ tr♦♥✐❝s ❬✷❪✱ ❛♥❞ ♣❤♦t♦♥✐❝s ❬✸✱ ✹❪ ✇✐t❤ ❛♣♣❧✐❝❛t✐♦♥s ❛s ❤✐❣❤❧② s❡♥s✐t✐✈❡ s❡♥s♦rs ❬✺❪ ❛♥❞ ❡♥❡r❣②✲r❡❧❛t❡❞ t❡❝❤♥♦❧♦❣✐❡s ❬✻✱ ✼❪✳ ❙✐❣♥✐✜❝❛♥t ❛❞✈❛♥❝❡♠❡♥ts ✐♥ ❢❛❜✲ r✐❝❛t✐♦♥ t❡❝❤♥✐q✉❡s ❛♥❞ t❤❡ ✇✐❞❡r r❛♥❣❡ ♦❢ t❡❝❤♥♦❧♦❣✐❝❛❧ ❛♣♣❧✐❝❛t✐♦♥s ❤❛✈❡❣❡♥❡r❛t❡❞ ✐♥❝r❡❛s❡❞ ✐♥t❡r❡st","cbCaijLnVOYh7zyb","https://ap.wps.com/l/cbCaijLnVOYh7zyb","pdf",2080019,1,30,"English","en",105,"# Abstract\n# Methods and modeling approach\n## Machine learning integration with atomistic simulations\n# Results and validation","[{\"question\":\"What is the main focus of this paper?\",\"answer\":\"The paper focuses on using machine learning to analyze mechanical behavior in silicon nanowires at the atomistic level, combining simulation data with predictive models.\"},{\"question\":\"How is the machine learning component used?\",\"answer\":\"Machine learning models are integrated with atomistic simulation workflows to learn mechanical trends and provide faster analysis of stress–strain behavior.\"},{\"question\":\"How are the results verified?\",\"answer\":\"The approach includes validation by comparing model predictions against simulation-based outcomes to assess accuracy and generalization under different conditions.\"}]","Machine learning-driven atomistic analysis of mechanical behavior in silicon nanowires - Accepted Version | PDF",1785723136,76,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-driven-atomistic-analysis-of-mechanical-behavior-in-silicon-nanowires-accepted-version","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-driven-atomistic-analysis-of-mechanical-behavior-in-silicon-nanowires-accepted-version/119216/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main focus of this paper?","Question",{"text":76,"@type":77},"The paper focuses on using machine learning to analyze mechanical behavior in silicon nanowires at the atomistic level, combining simulation data with predictive models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the machine learning component used?",{"text":81,"@type":77},"Machine learning models are integrated with atomistic simulation workflows to learn mechanical trends and provide faster analysis of stress–strain behavior.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the results verified?",{"text":85,"@type":77},"The approach includes validation by comparing model predictions against simulation-based outcomes to assess accuracy and generalization under different conditions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]