[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121158-en":3,"doc-seo-121158-105":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},121158,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning approaches for improving atomic force microscopy instrumentation and data analytics","Atomic force microscopy (AFM) enables high-resolution topographical imaging and nanomechanical property mapping, including measurements on stiff and soft samples such as live cells, proteins, and other biomolecules. Yet AFM operation and data analytics remain time-consuming and require specialized expertise and continuous oversight. Researchers increasingly apply artificial intelligence and deep learning to enhance AFM performance, accelerate data analytics, and enable high-throughput measurement workflows. This review surveys ML methods for instrumentation improvements, faster analysis, and key application areas, outlining future opportunities.","TYPE Review  \nPUBLISHED 24 September 2024 DOI 10.3389/fphy.2024.1347648  \nOPEN ACCESS  \nEDITED BY  \nMarco Capitanio,  \nUniversity of Florence, Italy  \nREVIEWED BY  \nCarlos Marcuello,  \nInstituto de Nanociencia y Materiales de Aragón (INMA), Spain  \nLuciana Magalhães Rebelo Alencar, Federal University of Maranhão, Brazil Umberto Celano,  \nArizona State University, United States  \n*CORRESPONDENCE  \nAnwesha Sarkar,  \n [anweshas@iastate.edu](anweshas@iastate.edu)  \n†These authors have contributed equally to this work  \nRECEIVED 01 December 2023  \nACCEPTED 11 September 2024  \nPUBLISHED 24 September 2024  \nCITATION  \nMasud N, Rade J, Hasib MHH, Krishnamurthy A and Sarkar A (2024) Machine learning approaches for improving atomic force microscopy instrumentation and data analytics. Front. Phys. 12:1347648 .  \ndoi: 10.3389/fphy.2024.1347648  \nCOPYRIGHT  \n© 2024 Masud, Rade, Hasib, Krishnamurthy and Sarkar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning approaches for improving atomic force microscopy instrumentation and data analytics  \nNabila Masud 1†, Jaydeep Rade 1†, Md. Hasibul Hasan Hasib 1, Adarsh Krishnamurthy 1,2 and Anwesha Sarkar 1*  \n1Electrical and Computer Engineering, Iowa State University, Ames, IA, United States, 2Mechanical Engineering, Iowa State University, Ames, IA, United States  \nAtomic force microscopy (AFM) is a part of the scanning probe microscopy family. It provides a platform for high-resolution topographical imaging, surface analysis as well as nanomechanical property mapping for stiff and soft samples (live cells, proteins, and other biomolecules) . AFM is also crucial for measuring singlemolecule interaction forces and important parameters of binding dynamics for receptor-ligand interactions or protein-protein interactions on live cells. However, performing AFM measurements and the associated data analytics are tedious, laborious experimental procedures requiring specific skill sets and continuous user supervision. Significant progress has been made recently inartificial intelligence (AI) and deep learning (DL), extending into microscopy. In this review, we summarize how researchers have implemented machine learning approaches so far to improve the performance of atomic force microscopy (AFM), make AFM data analytics faster, and make data measurement procedures high-throughput. We also shed some light on the different application areas of AFM that have significantly benefited from applications of machine learning frameworks and discuss the scope and future possibilities of these crucial approaches.  \nKEYWORDS  \natomic force microscopy, nanomechanical properties, artificial intelligence, machine learning, deep learning  \n1 Introduction  \nMembers of the scanning probe microscopy (SPM) family, such as atomic force microscopy (AFM) [1, 2], scanning tunneling microscopy (STM) [3] and scanning near-field optical microscopy (SNOM) [4] possess sharp probes to scan the surface of the substrate and measure physical quantities, i.e., tip-sample interaction force as a function of tip-sample separation distance and quantum tunneling current, respectively. Singlemolecule techniques such as AFM [5, 6], optical tweezers [7, 8], and magnetic tweezers [9, 10] have established themselves as powerful experimental techniques to investigate single molecules at a time producing crucial information about the topographical features, and parameters of binding kinetics [11, 12]. AFM [2, 6, 13–19] has several advantages over other single-molecule techniques as it can perform high-resolution 2.5D imaging and nanomechanical pro","cbCaip1ovw9mY1S5","https://ap.wps.com/l/cbCaip1ovw9mY1S5","pdf",3436741,1,17,"English","en",105,"# Introduction\n## AFM and single-molecule capabilities\n## Motivation for ML-assisted AFM\n## Review scope and contribution\n# Overview of ML-enhanced AFM workflow","[{\"question\":\"Why is atomic force microscopy (AFM) important for nanoscale analysis?\",\"answer\":\"AFM provides high-resolution topographical imaging and nanomechanical property mapping. It also supports measurements of single-molecule interaction forces and binding dynamics on samples such as live cells and biomolecules.\"},{\"question\":\"What challenges in AFM measurement motivate the use of machine learning?\",\"answer\":\"AFM experiments and data analytics are labor-intensive and require specific expertise and continuous supervision. The review highlights the need to reduce effort and improve throughput using AI/DL methods.\"},{\"question\":\"How can machine learning improve AFM instrumentation and data analytics according to the review?\",\"answer\":\"Machine learning can assist multiple steps, including sample or scanning-site selection, improving scanning processes, accelerating data analytics, and creating virtual AFM outputs such as synthetic AFM images.\"}]","Machine learning approaches for improving atomic force microscopy instrumentation and data analytics | PDF",1785734140,43,{"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-approaches-for-improving-atomic-force-microscopy-instrumentation-and-data-analytics","",{"@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-approaches-for-improving-atomic-force-microscopy-instrumentation-and-data-analytics/121158/",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","2026-08-03",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 is atomic force microscopy (AFM) important for nanoscale analysis?","Question",{"text":75,"@type":76},"AFM provides high-resolution topographical imaging and nanomechanical property mapping. It also supports measurements of single-molecule interaction forces and binding dynamics on samples such as live cells and biomolecules.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges in AFM measurement motivate the use of machine learning?",{"text":80,"@type":76},"AFM experiments and data analytics are labor-intensive and require specific expertise and continuous supervision. The review highlights the need to reduce effort and improve throughput using AI/DL methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How can machine learning improve AFM instrumentation and data analytics according to the review?",{"text":84,"@type":76},"Machine learning can assist multiple steps, including sample or scanning-site selection, improving scanning processes, accelerating data analytics, and creating virtual AFM outputs such as synthetic AFM images.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"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":106,"slug":138},19,"General","general"]