[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290730-105":59,"doc-detail-290730-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-for-optical-scanning-probe-nanoscopy","Machine Learning for Optical Scanning Probe Nanoscopy","","Machine learning and artificial intelligence are applied to advance scattering-type scanning near-field optical microscopy (s-SNOM) and related scanning-probe optical nanoscopy. The document argues that AI/ML-enhanced acquisition and analysis can make these measurements more efficient, accurate, and intelligent while addressing the growing volume of raw experimental data. It also situates ML’s broader impact across physics, optics/photonics, quantum optics, STEM image processing, and scanning probe microscopy, then highlights that ML research at the optics–SPM interface remains early.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-for-optical-scanning-probe-nanoscopy/290730/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-for-optical-scanning-probe-nanoscopy/290730.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What technique is central to the document’s discussion of optical nanoscopy?","Question",{"text":113,"@type":114},"Scattering-type scanning near-field optical microscopy (s-SNOM) is presented as the key technique, and the document connects it to scanning probe research and optical nanoscopy more broadly.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Why does the document claim ML and AI are especially valuable for scanning probe optical experiments?",{"text":118,"@type":114},"It emphasizes the urgent need to extract meaningful information from the rapidly growing volume of raw data, for which ML and AI are well suited.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the document position ML work at the optics–SPM interface?",{"text":122,"@type":114},"It states that research combining ML/AI with optical scanning probe microscopy such as s-SNOM is still in its infancy compared with established uses in other physics and microscopy subfields.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290730,1789642456,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":36,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nMachine Learning for Optical Scanning Probe Nanoscopy  \nPermalink  \n[https://escholarship.org/uc/item/75f000mv](https://escholarship.org/uc/item/75f000mv)  \nJournal  \nAdvanced Materials, 35(34)  \nISSN  \n0935-9648  \nAuthors  \nChen, Xinzhong  \nXu, Suheng Shabani, Sara et al.  \nPublication Date  \n2023-08-01  \nDOI  \n10.1002/adma.202109171  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, availalbe at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPersPective  \n[www.advmat.de](www.advmat.de)  \nMachine Learning for Optical Scanning Probe Nanoscopy  \nXinzhong Chen, Suheng Xu, Sara Shabani, Yueqi Zhao, Matthew Fu, Andrew J. Millis, Michael M. Fogler, Abhay N. Pasupathy, Mengkun Liu,* and D. N. Basov*  \nThe ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial-intelligence (AI) and machine-learning (ML) algorithms. Augmented with AI-and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.  \nare generated and collected on a daily basis. The sheer volume of the raw data urgently calls for a systematic way of extracting the information hidden within, which is exactly where ML and AI shine. Historically stemming from computer and data science, ML has quickly found legions of applications in physical sciences research thanks to its unique ability to mine nontrivial information from data. [1] Capitalizing on the availability and accessibility of large volumes of data from research facilities and novel simulation platforms, ML provides a systematic approach to unveiling “hidden” information across many branches of physics. [2–5]  \nThe idea of using ML to study physics  \n1. Introduction  \nMachine learning (ML) and artificial intelligence (AI) are revolutionizing a wide range of industries and sciences at an unprecedented pace. A properly trained AI agent is capable of performing a series of complex tasks ranging from comprehending information conveyed by human speech to maneuvering autonomous vehicles in busy traffic. Although ML/AI concepts and algorithms such as the artificial neural network (ANN) were developed decades ago, efficient training of deep ML models has only been enabled by the recent advances in computer architecture and hardware. At the same time, a tremendous amount of data, ranging from customers' shopping preferences on an e-commerce site to particle trajectories from the Large Hadron Collider,  \nX. Chen, M. Liu  \nDepartment of Physics and Astronomy Stony Brook University  \nStony Brook, NY 11794, USA [E-mail: mengkun.liu@stonybrook.edu](E-mail: mengkun.liu@stonybrook.edu)  \nS. Xu, S. Shabani, M. Fu, A. J. Millis, A. N. Pasupathy, D. N. Basov  \nDepartment of Physics Columbia University New York, NY 10027, USA  \nE-mail: [db3056@columbia.edu](db3056@columbia.edu)  \nY. Zhao, M. M. Fogler Department of Physics University of California at San Diego La Jolla, CA 92093-0319, USA  \nM. Liu  \nNational Synchrotron Light Source II Brookhaven National Laboratory Upton, NY 11973, USA  \nThe ORCID identification number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/adma.202109171](https://doi.org/10.1","cbCaisSM6D8bhAjL","https://ap.wps.com/l/cbCaisSM6D8bhAjL","pdf",2103165,"English","# Introduction\n## The role of ML/AI in modern sciences\n## ML applications across physics, optics, and microscopy","[{\"question\":\"What technique is central to the document’s discussion of optical nanoscopy?\",\"answer\":\"Scattering-type scanning near-field optical microscopy (s-SNOM) is presented as the key technique, and the document connects it to scanning probe research and optical nanoscopy more broadly.\"},{\"question\":\"Why does the document claim ML and AI are especially valuable for scanning probe optical experiments?\",\"answer\":\"It emphasizes the urgent need to extract meaningful information from the rapidly growing volume of raw data, for which ML and AI are well suited.\"},{\"question\":\"How does the document position ML work at the optics–SPM interface?\",\"answer\":\"It states that research combining ML/AI with optical scanning probe microscopy such as s-SNOM is still in its infancy compared with established uses in other physics and microscopy subfields.\"}]","Machine Learning for Optical Scanning Probe Nanoscopy | PDF"]