[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117199-en":3,"doc-seo-117199-105":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},117199,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine learning assisted multifrequency AFM - Force model prediction","Multifrequency atomic force microscopy (AFM) increases resolving power, adds complementary contrast channels, and enables pixel-by-pixel quantification of material properties. However, it cannot directly extract and analyze force profiles to validate a chosen force model during scanning. A data-driven machine learning framework is introduced to predict the optimal force model from multifrequency AFM observables at each pixel, trained on simulation data. The correct force model then supports analytic recovery of material properties using the multifrequency AFM formalism.","RESEARCH ARTICLE | DECEMBER 05 2023  \nMachine learning assisted multifrequency AFM: Force model prediction  \nLamiaa Elsherbiny  ; Sergio Santos 􀀧  ; Karim Gadelrab  ; Tuza Olukan  ; Josep Font  ; Victor Barcons  ; Matteo Chiesa 􀀧   \nAppl. Phys. Lett. 123, 231603 (2023)  \n[https://doi.org/10.1063/5.0176688](https://doi.org/10.1063/5.0176688)  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \nCrossMark  \n04 January 2024 09:07:54  \nApplied Physics Letters ARTICLE  \n[pubs.aip.org/aip/apl](pubs.aip.org/aip/apl)  \nMachine learning assisted multifrequency AFM: Force model prediction  \n\n| Cite as: Appl. Phys. Lett. 123, 231603 (2023); doi: 10.1063/5.0176688 Submitted: 15 September 2023 . Accepted: 20 November 2023 .\u003Cbr>Published Online: 5 December 2023 |  |  |  |\n| --- | --- | --- | --- |\n| Lamiaa Elsherbiny,1  Sergio Santos,2,a)  Karim Gadelrab,3  Tuza Olukan,1  Josep Font,4  Victor Barcons,4  and Matteo Chiesa1,2,a)  |  |  |  |\n| AFFILIATIONS\u003Cbr>1 Laboratory for Energy and NanoScience (LENS), Khalifa University of Science and Technology, Masdar Institute Campus, 127788 Abu Dhabi, United Arab Emirates\u003Cbr>2 Department of Physics and Technology, UiT-The Arctic University of Norway, 9037 Tromsø, Norway\u003Cbr>3 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA 4 Departament d’ Enginyeria Minera, Industrial i TIC, UPC BarcelonaTech, 08242 Manresa, Spain\u003Cbr>a)Authors to whom correspondence should be addressed: [ssantos78h@gmail.com and matteo.chiesa@ku.ac.ae](ssantos78h@gmail.com and matteo.chiesa@ku.ac.ae) |  |  |  |\n| ABSTRACT\u003Cbr>Multifrequency atomic force microscopy (AFM) enhances resolving power, provides extra contrast channels, and is equipped with a formalism to quantify material properties pixel by pixel. On the other hand, multifrequency AFM lacks the ability to extract and examine the profile to validate a given force model while scanning. We propose exploiting data-driven algorithms, i.e., machine learning packages, to predict the optimum force model from the observables of multifrequency AFM pixel by pixel. This approach allows distinguishing between different phenomena and selecting a suitable force model directly from observables. We generate predictive models using simulation data. Finally, the formalism of multifrequency AFM can be employed to analytically recover material properties by inputting the right force model.\u003Cbr>VC 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ([http://](http://)[ ](http://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/)). [https://doi.org/10.1063/5.0176688](https://doi.org/10.1063/5.0176688) |  |  |  |\n\nA large body of physics is dedicated to understanding the behavior of forces.1–3 The atomic force microscope (AFM) is employed to understand and quantify force in the nanoscale4,5 with nanometric,6 atomic,7–9 and sub-atomic10 resolution. Forces provide information about samples and are employed to quantify and characterize properties, structure and functions of cells,5,11–13 biomolecular processes,14 thin and ultrathin films,15 and a range of other interesting phenomena16 including magnetic17 and hydration forces.18 Nanoscale forces might vary in terms of the shape of their profile and/or their magnitude or strength.2 The particular phenomena involved in the interaction is responsible for the specific shape of the force profile, whereas the magnitude is a characteristic of the strength of the interaction.2 In dynamic AFM, the force profile can be reconstructed by acquiring curves where observable parameters, such as phase, amplitude,19,20 or frequency shift,21,22 are obtained in terms of cantilever–sample distance. Experimentally monitored data are then transformed into force distance curves. 19,21,23 In order to quantify material properties, a second step is required. This involves considering a force model that reasonabl","cbCaikqUjUzVcXBE","https://ap.wps.com/l/cbCaikqUjUzVcXBE","pdf",1155559,1,7,"English","en",105,"# Abstract\n## Motivation: limits of force-model validation in multifrequency AFM\n## Proposed method: machine-learning prediction per pixel\n## Training data: simulation-based predictive models\n## Outcome: analytic material property recovery with the chosen force model","[{\"question\":\"What limitation does multifrequency AFM have in force-model validation?\",\"answer\":\"Multifrequency AFM can quantify properties, but it lacks the ability to extract and examine the force profile needed to validate a specific force model while scanning.\"},{\"question\":\"How does the proposed approach select the force model?\",\"answer\":\"It uses data-driven machine learning to predict the optimum force model directly from the multifrequency AFM observables on a pixel-by-pixel basis.\"},{\"question\":\"What data are used to build the predictive models?\",\"answer\":\"Predictive models are generated using simulation data, enabling the framework to associate measured observables with suitable force-model choices.\"}]","Machine learning assisted multifrequency AFM - Force model prediction | PDF",1785674378,18,{"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},"machine-learning-assisted-multifrequency-afm-force-model-prediction","",{"@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/machine-learning-assisted-multifrequency-afm-force-model-prediction/117199/",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 limitation does multifrequency AFM have in force-model validation?","Question",{"text":75,"@type":76},"Multifrequency AFM can quantify properties, but it lacks the ability to extract and examine the force profile needed to validate a specific force model while scanning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach select the force model?",{"text":80,"@type":76},"It uses data-driven machine learning to predict the optimum force model directly from the multifrequency AFM observables on a pixel-by-pixel basis.",{"name":82,"@type":73,"acceptedAnswer":83},"What data are used to build the predictive models?",{"text":84,"@type":76},"Predictive models are generated using simulation data, enabling the framework to associate measured observables with suitable force-model choices.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"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"]