[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119678-en":3,"doc-seo-119678-105":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},119678,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning for Optical Scanning Probe Nanoscopy - research perspective","Nanometer-scale optical imaging and spectroscopy are essential for revealing low-energy effects in quantum materials and for extracting vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. The paper highlights scattering-type scanning near-field optical microscopy (s-SNOM) as a broadly adopted technique and argues that combining it with scanning-probe research can be strengthened by artificial intelligence and machine-learning algorithms. AI/ML-enhanced acquisition and analysis are presented as enabling more efficient, accurate, and intelligent optical nanoscopy by leveraging the growing volume of experimental and simulated data.","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, available 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","cbCaicjwG8FOaGrF","https://ap.wps.com/l/cbCaicjwG8FOaGrF","pdf",2156810,1,16,"English","en",105,"# Introduction\n## The role of ML/AI across physics and sciences\n## Optical and photonics applications\n## ML/AI for scanning probe microscopy\n## The emerging interface: ML and AI on s-SNOM\n## Perspective and potential applications","[{\"question\":\"Why is nanometer-scale optical imaging and spectroscopy important?\",\"answer\":\"It helps decipher low-energy effects in quantum materials and enables vibrational fingerprint detection in diverse samples such as planetary/extra-terrestrial particles, catalysts, and aqueous biological specimens.\"},{\"question\":\"What technique is central to this perspective?\",\"answer\":\"Scattering-type scanning near-field optical microscopy (s-SNOM), a scanning-probe optical nanoscopy method used across multiple research fields.\"},{\"question\":\"How can AI and ML improve s-SNOM and scanning-probe optical nanoscopy?\",\"answer\":\"By enhancing data acquisition and analysis, ML/AI can make measurements more efficient, accurate, and intelligent, addressing the large volume of raw data collected daily.\"}]","Machine Learning for Optical Scanning Probe Nanoscopy - 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