[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119062-en":3,"doc-seo-119062-105":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":4,"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},119062,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)","Machine learning methods for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM), face practical barriers due to the difficulty of acquiring large, high-quality, well-annotated experimental training datasets and the resulting risk of dataset-dependent failures and limited out-of-distribution generalization. A robust synthetic-data strategy is required to cover imaging conditions and sample variety without human bias. Construction Zone, a Python package, enables rapid generation of realistic nanoscale atomic structures and supports an end-to-end synthetic-data workflow for training neural networks to segment nanoparticles in experimental HRTEM images, including a systematic study of simulation fidelity and imaging/structure distributions that drive benchmark performance.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nA robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)  \nPermalink  \n[https://escholarship.org/uc/item/9n94c392](https://escholarship.org/uc/item/9n94c392)  \nJournal  \nnpj Computational Materials, 10(1)  \nISSN  \n2057-3960  \nAuthors  \nRangel DaCosta, Luis  \nSytwu, Katherine Groschner, CK et al.  \nPublication Date  \n2024  \nDOI  \n10.1038/s41524-024-01336-0  \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  \nnpj | computational materials Article  \nPublished in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences  \n[https://doi.org/10.1038/s41524-024-01336-0](https://doi.org/10.1038/s41524-024-01336-0)  \nA robust synthetic data generation framework for machine learning in highresolution transmission electron microscopy (HRTEM)  \n Check for updates  \n\n| Luis Rangel DaCosta 1,2 , Katherine Sytwu 2, C. K. Groschner1,2 & M. C. Scott 1,2  |  |\n| --- | --- |\n| Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM) . However, successfully implementing such machine learning tools can be difficult duetothe challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM imageson thetask of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, weare ableto achieve state-ofthe-art segmentation performance on these experimental benchmarksand, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at [https://github](https://github). com/lerandc/construction_zone. |  |\n| Machine learning (ML) methods promise to accurately and automatically analyze large datasets at high-speeds, revolutionizing our materials characterization workflows. Many state-of-the-art ML tools rely on supervised learning techniques, where models utilize large amounts of data annotated with features of interest for training. The performance of supervised ML models, like neural networks, directly depends on the contents and generating distribution ofthe dataset used for model training, and, importantly, such models have been shown to extrapolate poorly beyond their training datasets1 and have limited out-of-distribution generalization behavior2,3. Developing robust ML models for automated analysis of transmission electron microscopy (TEM), a versatile technique for structural and functional materials characterization at the atomic-scale, thus requires large image datasets which fully cover experimental imaging conditions and the variety of samples one has imaged. However, manually producing | sufficiently large and diverse sets of well-","cbCaijKXtAS76L6X","https://ap.wps.com/l/cbCaijKXtAS76L6X","pdf",2497907,1,12,"English","en",105,"# Construction Zone and synthetic structure generation\n## End-to-end ML workflow for HRTEM segmentation\n## Data curation factors and benchmark evaluation\n## Strategies for robust performance with synthetic-only data","[{\"question\":\"Why is synthetic data important for machine learning in HRTEM tasks?\",\"answer\":\"Experimental training data is costly and labor intensive to produce at sufficient scale and quality, and supervised models can extrapolate poorly beyond their training distributions. Synthetic datasets can cover imaging conditions with ground-truth, physics-based annotations while avoiding human bias and errors.\"},{\"question\":\"What is Construction Zone and what does it generate?\",\"answer\":\"Construction Zone is a Python package for rapid generation of complex nanoscale atomic structures. It enables fast, systematic sampling of realistic nanomaterial structures and can function as a random structure generator for producing large, diverse synthetic datasets.\"},{\"question\":\"How does the paper evaluate which synthetic data choices improve model performance?\",\"answer\":\"It studies data curation by examining how simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions affect model performance across three benchmark experimental HRTEM image datasets.\"}]","A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM) | PDF",1785722143,30,{"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},"a-robust-synthetic-data-generation-framework-for-machine-learning-in-high-resolution-transmission-electron-microscopy-hrtem","",{"@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/a-robust-synthetic-data-generation-framework-for-machine-learning-in-high-resolution-transmission-electron-microscopy-hrtem/119062/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is synthetic data important for machine learning in HRTEM tasks?","Question",{"text":75,"@type":76},"Experimental training data is costly and labor intensive to produce at sufficient scale and quality, and supervised models can extrapolate poorly beyond their training distributions. Synthetic datasets can cover imaging conditions with ground-truth, physics-based annotations while avoiding human bias and errors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is Construction Zone and what does it generate?",{"text":80,"@type":76},"Construction Zone is a Python package for rapid generation of complex nanoscale atomic structures. It enables fast, systematic sampling of realistic nanomaterial structures and can function as a random structure generator for producing large, diverse synthetic datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate which synthetic data choices improve model performance?",{"text":84,"@type":76},"It studies data curation by examining how simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions affect model performance across three benchmark experimental HRTEM image datasets.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]