[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124444-en":3,"doc-seo-124444-105":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},124444,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","On-the-fly machine learning force fields for alkali silicate glasses","Molecular dynamics simulations provide atomic-resolution structure of glassy materials over short time scales, but they require accurate and efficient force fields. Focusing on alkali silicate glasses, this work develops transferable MLFFs usable across broad temperature and composition ranges. On-the-fly machine learning routines generate energy and force predictions comparable to density functional theory. The potential energy surface is encoded using structural descriptors combining radial and angular information in a reduced-dimensional representation. Trained MLFFs enable simulations of silica and Li2O/Na2O/K2O silicates and support composition-dependent structural analysis via neutron structure factors, distribution functions, coordination numbers, and Qn speciation, including reproduction of experimental splitting of the first sharp diffraction peak in potassium silicate glasses.","Aalborg Universitet  \nOn-the-fly machine learning force fields for alkali silicate glasses  \nGanisetti, Sudheer; Du, Tao; Krishnan, N. M. Anoop; Smedskjaer, Morten M.  \nPublished in:  \nPhysical Review Materials  \nDOI (link to publication from Publisher):  \n10.1103/qrgw-zxhf  \nPublication date:  \n2025  \nDocument Version  \nAccepted author manuscript, peer reviewed version  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nGanisetti, S. , Du, T. , Krishnan, N. M. A. , & Smedskjaer, M. M. (2025) . On-the-fly machine learning force fields for alkali silicate glasses. Physical Review Materials, 9(11), Article 115601. [https://doi.org/10.1103/qrgw-zxhf](https://doi.org/10.1103/qrgw-zxhf)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public 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.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from [vbn.aau.dk](vbn.aau.dk) on: August 03, 2026  \nOn-the-fly machine learning force fields for alkali silicate glasses  \nSudheer Ganisetti 1, Tao Du 1,2, N. M. Anoop Krishnan3, Morten M. Smedskjaer 1,*  \n1Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark  \n2Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, HongKong 999077, China 3Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India  \n* [Corresponding author. e-mail: mos@bio.aau.dk](Corresponding author. e-mail: mos@bio.aau.dk)  \nAbstract  \nMolecular dynamics simulations can provide structural information of glass materials with atomic resolution and for very short time scales. In turn, this requires accurate and effective force fields. Here, focusing on alkali silicate glasses, we develop transferable force fields that are applicable across a wide range of temperatures and compositions. Using on-the-fly machine learning routines, these force fields produce energy and force predictions with accuracy comparable to that of density functional theory. The potential energy landscape is described using structural descriptors that incorporate both radial and angular information within a reduceddimensional descriptor space. We then employ these accurate machine learning force fields (MLFF) to prepare silica and binary alkali (Li2O, Na2O, K2O) silicate glass samples, allowing us to study their structures. We assess the composition dependence of the structural characteristics of the glasses by computing several key parameters, including neutron structure factor, pair distribution function, angular distribution function, coordination number, and Qn speciation. The trained MLFFs accurately reproduce the splitting of the first sharp diffraction peak in potassium silicate glasses, consistent with experimental findings, and reveal that its structural origin is tied to Si-K interactions. Overall, our research demonstrates the power of combining advanced computational techniques with classical structural analysis methods to enhance our structural understanding of complex glass systems.  \nKeywords: alkali silicate glasses, machine leaning force fields, composition-structure, Qn-distribution, molecular dynamics simulations.  \n1. Introduction  \nSilicate glasses play a crucial role in advancing technology across diverse indust","cbCaigjKOhA0Wfur","https://ap.wps.com/l/cbCaigjKOhA0Wfur","pdf",2633972,1,25,"English","en",105,"# Abstract\n# Introduction\n## Composition-structure correlations in silicate glasses\n## Network formers, bridging oxygens, and modifiers\n## Structural descriptors across length scales","[{\"question\":\"Why are accurate force fields essential for molecular dynamics simulations of glasses?\",\"answer\":\"They are needed to reproduce atomic-resolution structure over short time scales. Accurate and effective force fields ensure reliable energy and force predictions during simulations.\"},{\"question\":\"What is the main idea behind the developed on-the-fly machine learning force fields?\",\"answer\":\"The study builds transferable MLFFs for alkali silicate glasses that remain applicable across a wide range of temperatures and compositions. On-the-fly learning produces energy and force accuracy comparable to density functional theory.\"},{\"question\":\"How do the MLFF simulations help characterize glass structure and validate results?\",\"answer\":\"The simulations compute neutron structure factor, pair and angular distribution functions, coordination numbers, and Qn speciation. For potassium silicate glasses, the MLFF reproduces experimental splitting of the first sharp diffraction peak, attributing the origin to Si–K interactions.\"}]","On-the-fly machine learning force fields for alkali silicate glasses | PDF",1785822333,63,{"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},"on-the-fly-machine-learning-force-fields-for-alkali-silicate-glasses","",{"@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/on-the-fly-machine-learning-force-fields-for-alkali-silicate-glasses/124444/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are accurate force fields essential for molecular dynamics simulations of glasses?","Question",{"text":75,"@type":76},"They are needed to reproduce atomic-resolution structure over short time scales. Accurate and effective force fields ensure reliable energy and force predictions during simulations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main idea behind the developed on-the-fly machine learning force fields?",{"text":80,"@type":76},"The study builds transferable MLFFs for alkali silicate glasses that remain applicable across a wide range of temperatures and compositions. On-the-fly learning produces energy and force accuracy comparable to density functional theory.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the MLFF simulations help characterize glass structure and validate results?",{"text":84,"@type":76},"The simulations compute neutron structure factor, pair and angular distribution functions, coordination numbers, and Qn speciation. 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