[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125910-en":3,"doc-seo-125910-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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},125910,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhancing Scanning Tunneling Microscopy with Automation and Machine Learning","The scanning tunneling microscope (STM) enables atomic-resolution imaging and manipulation, yet suffers from time-consuming bottlenecks including probe conditioning, tip instability, and noise artifacts that limit experimental throughput. This dissertation presents research efforts to mitigate these issues using automation and machine learning. It is organized into two sections covering eight studies: nanoscale fabrication and tip preparation, followed by neural-network-based analysis of STM images and spectroscopy, including identification of conditioned tips and atomic-scale defects.","University of Central Florida  \nSTARS  \nGraduate Thesis and Dissertation 2023-2024  \n2024  \nEnhancing Scanning Tunneling Microscopy with Automation and Machine Learning  \nDarian Smalley  \nUniversity of Central Florida  \n Part of the Materials Science and Engineering Commons, and the Physics Commons Find similar works at: [https://stars.library.ucf.edu/etd2023](https://stars.library.ucf.edu/etd2023)  \nUniversity of Central Florida Libraries [http://library.ucf.edu](http://library.ucf.edu)  \nThis Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Graduate Thesis and Dissertation 2023-2024 by an authorized administrator of STARS. For more information, please [contact](contact STARS@ucf.edu)[ STARS@ucf.edu](contact STARS@ucf.edu).  \nSTARS Citation  \nSmalley, Darian, \"Enhancing Scanning Tunneling Microscopy with Automation and Machine Learning\"(2024) . Graduate Thesis and Dissertation 2023-2024. 207.  \n[https://stars.library.ucf.edu/etd2023/207](https://stars.library.ucf.edu/etd2023/207)  \nENHANCING SCANNING TUNNELING MICROSCOPY WITH AUTOMATION AND MACHINE  \nLEARNING  \nby  \nDARIAN SMALLEY  \nB.S. Computer Science, University of Central Florida, 2019  \nB.S. Physics, University of Central Florida, 2019  \nM.S. Physics, University of Central Florida, 2024  \nA dissertation submitted in partial fulfillment of the requirements  \nfor the degree of Doctor of Philosophy  \nin the Department of Physics  \nin the College of Sciences  \nat the University of Central Florida  \nOrlando, Florida  \nSpring Term  \n2024  \nMajor Professor: Masahiro Ishigami  \n© 2024 Darian Smalley  \nABSTRACT  \nThe scanning tunneling microscope (STM) is one of the most advanced surface science tools capable of atomic resolution imaging and atomic manipulation. Unfortunately, STM has many timeconsuming bottlenecks, like probe conditioning, tip instability, and noise artificing, which causes the technique to have low experimental throughput. This dissertation describes my efforts to address these challenges through automation and machine learning. It consists of two main sections each describing four projects for a total of eight studies.  \nThe first section details two studies on nanoscale sample fabrication and two studies on STM tip preparation. The first two studies describe the fabrication of graphene-based Josephson Junction devices and the factorial optimization of patterned carbon nanotube forest synthesis. The second two studies focus on the factorial optimization of electrochemical STM tip etching and automated STM tip functionalization via in-situ silicon nanocolumn growth.  \nThe second section details four studies on the use of neural networks for STM image and spectroscopy analysis. The third two studies are on the effectiveness of convolutional neural networks for identifying images of conditioned STM tips on the Au(111) surface and on the detection and metrology of atomic scale defects in single crystal tungsten diselenide, a transition metal dichalcogenide. The fourth two studies are on the use of variational autoencoders to autonomously classify scanning tunneling spectra of various materials, molecules, and surface structures and to identify bismuth and nickel atoms from cross sectional STM images of doped gallium arsenide.  \nThis dissertation is dedicated to my beloved family and friends, some of whom are no longer here, who have been my Samwise Gamgee and carried me up Mount Doom to cast my ring into its fires. Without  \nthem, I would have fallen long ago.  \nACKNOWLEDGMENTS  \nThey say that luck is a combination of preparation and opportunity. I have been lucky to be surrounded by many wonderful people who have devoted far too much of their time and effort to helping me grow and develop as a scientist during my graduate career. I am happy to call attention at least a few of them here.  \nFirstly, none of this would have been possible without my introduction to experimental physics by ","cbCaiciT5kU6aNaJ","https://ap.wps.com/l/cbCaiciT5kU6aNaJ","pdf",10695512,1,199,"English","en",105,"# Abstract\n## Automation and machine learning to improve STM throughput\n## Nanoscale sample fabrication and STM tip preparation\n## Neural networks for STM image and spectroscopy analysis\n## Variational autoencoders for STM spectra classification and atom identification","[{\"question\":\"What problem does this dissertation address in scanning tunneling microscopy?\",\"answer\":\"It addresses key STM bottlenecks that reduce experimental throughput, including probe conditioning, tip instability, and noise artifacts.\"},{\"question\":\"What are the two main sections of the dissertation?\",\"answer\":\"One section focuses on nanoscale sample fabrication and STM tip preparation, and the second section focuses on neural-network methods for STM image and spectroscopy analysis.\"},{\"question\":\"How does the dissertation use machine learning in STM data analysis?\",\"answer\":\"It applies neural networks, including convolutional neural networks for conditioned tip imaging and defect detection, and variational autoencoders to classify scanning tunneling spectra and identify atoms from STM images.\"}]","Enhancing Scanning Tunneling Microscopy with Automation and Machine Learning | PDF",1785901984,501,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhancing-scanning-tunneling-microscopy-with-automation-and-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/enhancing-scanning-tunneling-microscopy-with-automation-and-machine-learning/125910/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does this dissertation address in scanning tunneling microscopy?","Question",{"text":76,"@type":77},"It addresses key STM bottlenecks that reduce experimental throughput, including probe conditioning, tip instability, and noise artifacts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the two main sections of the dissertation?",{"text":81,"@type":77},"One section focuses on nanoscale sample fabrication and STM tip preparation, and the second section focuses on neural-network methods for STM image and spectroscopy analysis.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the dissertation use machine learning in STM data analysis?",{"text":85,"@type":77},"It applies neural networks, including convolutional neural networks for conditioned tip imaging and defect detection, and variational autoencoders to classify scanning tunneling spectra and identify atoms from STM images.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]