[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125293-en":3,"doc-seo-125293-105":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},125293,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Application of Machine Learning in Real-Space Structural Characterization of Nanomaterials","Real-space structural characterization of nanomaterials is treated as creating a digital copy of a sample, yet the characterization is not complete. Compared with traditional ensemble averaging approaches such as diffraction, real-space characterization yields image-like data that remain “rawer” for quantitative scientific extraction. Crystal grains, defects, and polydispersity increase heterogeneity and complicate analysis. High-throughput imaging, time series imaging, and tomography amplify data volume and dimensionality, requiring efficient quantitative workflows. This dissertation applies advanced machine learning algorithms to enable accurate tracking, artifact correction, interpretable dimension reduction and clustering, and automated real-time tomography.","APPLICATION OF MACHINE LEARNING IN REAL-SPACE STRUCTURAL CHARACTERIZATION OF NANOMATERIALS  \nBY  \nLEHAN YAO  \nDISSERTATION  \nSubmitted in partial fulfillment of the requirements  \nfor the degree of Doctor of Philosophy in Materials Science and Engineering  \nin the Graduate College of the  \nUniversity of Illinois Urbana-Champaign, 2024  \nUrbana, Illinois  \nDoctoral Committee:  \nAssociate Professor Qian Chen, Chair and Director of Research  \nProfessor Charles M. Schroeder  \nAssistant Professor Antonia Statt  \nProfessor Catherine J. Murphy  \nABSTRACT  \nThe first step in real-space structural characterization of nanomaterials is essentially equivalent to the creation of a digital copy of the materials sample, where the characterization is far from finished. Unlike the traditional ensemble averaging characterization such as diffraction techniques, real-space characterization directly provides image-like data. Although the image-like data look more intuitive to human beings, they are considered “rawer” for the quantitative information extraction for scientific research. Meanwhile, the heterogeneous nature of nanomaterials such as crystal grains, defects, and polydispersity further exaggerates the difficulty in their data analysis. For a long time, real-space imaging only played the supporting roles in nanomaterials characterization. Examples include solely being displayed for demonstration or providing for the manual measurement of some local features. The recent development of real-space characterization techniques such as high-throughput imaging, time series imaging, and tomography further render the situation more serious by increasing the data volume and dimensionality. To ensure the efficiency in communications of scientific research, quantitative analysis has to be performed to transfer those large-volume real-space characterization data to digestible and concise conclusions. The recent advancements in machine learning and especially those image-based algorithms such as image classification and image segmentation undoubtedly open opportunities for real-space characterization data analysis. This dissertation intends to apply cutting-edge machine learning algorithms to tackle the challenges in real-space nanomaterials characterization. Specifically, supervised neural networks are employed to achieve the accurate nanoparticle tracking in liquid-phase transmission electron microscopy videos under high noise, and then an unsupervised neural network training workflow is developed to solve the missingwedge artifact in electron tomography, as examples of data processing in high-dimensional realspace characterization. Next, the applications of unsupervised machine learning on data interpretation are demonstrated, where dimension-reduction and clustering algorithms visualize and summarize the typical features presenting in the large-volume characterization data. Lastly, an automation of electron tomography is achieved by the real-time data processing and the feedback control of the equipment, where the fast electron tomography at continuous time points is realized, further increasing dimensionality of the real-space nanomaterials characterization.  \nACKNOWLEDGEMENTS  \nI thank my thesis advisor Professor Qian Chen, for the guidance on my research projects and for the suggestions on my career path. The scientific knowledge and research visions I learned from her will certainly benefit my academic career in the long run. I also give my special thanks to her for offering this opportunity [of Ph.D. study](of Ph.D. study) to me when I first joined Chen Group as a master student. This opportunity opened doors to numerous new possibilities in my life.  \nI thank my thesis committee members Professor Qian Chen, Professor Charles M. Schroeder, Professor Antonia Statt, and Professor Catherine J. Murphy, for their comments, suggestions, and continuous support of my thesis research. I also thank Professor Jian-Min Zuo for the helpful discussion and s","cbCaidQhidLIofix","https://ap.wps.com/l/cbCaidQhidLIofix","pdf",7711216,1,147,"English","en",105,"# Chapter 1 Introduction\n## Real-Space Nanomaterials Characterization and Data Dimensionality\n## The Rise of Machine Learning in Real-Space Nanomaterials Characterization\n## Overview of Machine Learning Methods Used in Real-Space Data Analysis\n## Scope of the Work: Data Processing, Data Interpretation, and Characterization Control\n# Chapter 2 Supervised Machine Learning to Reveal Nanoparticles Dynamics in Liquid-Phase TEM Videos\n## Introduction","[{\"question\":\"Why does real-space structural characterization require more advanced quantitative processing than diffraction-based methods?\",\"answer\":\"Real-space methods produce image-like data that are more intuitive visually but are “rawer” for quantitative extraction. This makes downstream quantitative analysis essential for scientific interpretation.\"},{\"question\":\"What machine learning approaches does the dissertation use for liquid-phase TEM video analysis?\",\"answer\":\"Supervised neural networks are used to achieve accurate nanoparticle tracking under high noise conditions in liquid-phase transmission electron microscopy videos.\"},{\"question\":\"How are missing-wedge artifacts addressed in electron tomography?\",\"answer\":\"An unsupervised neural network training workflow is developed to solve the missing-wedge artifact problem in electron tomography data.\"}]","Application of Machine Learning in Real-Space Structural Characterization of Nanomaterials | PDF",1785898020,370,{"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},"application-of-machine-learning-in-real-space-structural-characterization-of-nanomaterials","",{"@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/application-of-machine-learning-in-real-space-structural-characterization-of-nanomaterials/125293/",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","2026-08-05",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 does real-space structural characterization require more advanced quantitative processing than diffraction-based methods?","Question",{"text":75,"@type":76},"Real-space methods produce image-like data that are more intuitive visually but are “rawer” for quantitative extraction. This makes downstream quantitative analysis essential for scientific interpretation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approaches does the dissertation use for liquid-phase TEM video analysis?",{"text":80,"@type":76},"Supervised neural networks are used to achieve accurate nanoparticle tracking under high noise conditions in liquid-phase transmission electron microscopy videos.",{"name":82,"@type":73,"acceptedAnswer":83},"How are missing-wedge artifacts addressed in electron tomography?",{"text":84,"@type":76},"An unsupervised neural network training workflow is developed to solve the missing-wedge artifact problem in electron tomography data.","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"]