[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127221-en":3,"doc-seo-127221-105":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":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},127221,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering","Machine learning-enhanced atom probe tomography (ML-APT) enables near-atomically resolved mining of atom probe data to deliver 3D quantitative characterization of chemical short-range order (CSRO). The method exploits APT’s high elemental sensitivity to reveal multiple CSRO configurations in a CoCrNi medium-entropy alloy, supported by advanced Monte-Carlo simulations. Quantitative comparisons connect processing parameters with physical properties, providing unambiguous CSRO morphology, domain density, and atomic configurations to refine atomic-scale materials design strategies.","Vol. 36 • No. 44 • November 1 • 2024  \n[www.advmat.de](www.advmat.de)  \nRESEARCH ARTICLE  \n[www.advmat.de](www.advmat.de)  \nMachine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering  \nYue Li,* Timoteo Colnaghi, Yilun Gong,* Huaide Zhang, Yuan Yu, Ye Wei, Bin Gan, Min Song, Andreas Marek, Markus Rampp, Siyuan Zhang, Zongrui Pei, Matthias Wuttig, Sheuly Ghosh, Fritz Körmann, Jörg Neugebauer, Zhangwei Wang,* and Baptiste Gault*  \nIn solids, chemical short-range order (CSRO) refers to the self-organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning-enhanced atom probe tomography (APT) can mine the near-atomically resolved APT data and jointly exploit the technique’s high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium-entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state-ofthe-art Monte-Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic-scale architectures.  \ncompositions, structures, and imperfections from the macro- to microscale and even atomic scale. Alloy making traditionally involves the introduction of small quantities of one or more species, solutes, into a matrix of a solvent element. During processing, one or more (meta)stable phases form that modify the response to physical or mechanical stimulation. [1] In the past decade, so-called high/mediumentropy alloys (H/MEAs) have been introduced, whereby multiple elements are mixed in equal, or close to equal quantity. Although initially assumed to be chemically disordered,[2] i.e., atoms from these principal elements randomly occupy sites of the crystalline lattice, recent studies have suggested that atomicscale, chemical short-range order (CSRO) is far more prevalent in H/MEAs than  \n1. Introduction  \nOver time, strategies were developed to tailor the properties of materials to societal needs, by manipulating their  \ninitially assumed, offering a potential lever to tailor their properties. [2b,3]  \nA representative H/MEA is CoCrNi, in which the presence and nature of CSRO are currently debated. [2b,3b,d–g,4] Transmission  \nY. Li, Y. Gong, S. Zhang, S. Ghosh, F. Körmann, J. Neugebauer, B. Gault Max-Planck-Institut für Eisenforschung GmbH  \nMax-Planck-Straße 1, 40237 Düsseldorf, Germany  \nE-mail: [yue.li@mpie.de](yue.li@mpie.de); [y.gong@mpie.de](y.gong@mpie.de); [b.gault@mpie.de](b.gault@mpie.de)  \nT. Colnaghi, A. Marek, M. Rampp Max Planck Computing and Data Facility Gießenbachstraße 2, 85748 Garching, Germany  \nY. Gong  \nDepartment of Materials University of Oxford  \nParks Road, Oxford OX1 3PH, UK  \nH. Zhang, Y. Yu, M. Wuttig Institute of Physics (IA) RWTH Aachen University  \n52056 Aachen, Germany  \nThe ORCID identification number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/adma.202407564](https://doi.org/10.1002/adma.202407564)[ ](https://doi.org/10.1002/adma.202407564)© 2024 The Author(s). Advanced Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.1002/adma.202407564  \nY. Wei  \nEcole Polytechnique Fédérale de Lausanne School of Engineering  \nRte Cantonale, Lausanne 1015, Switzerland  \nB. Gan  \nSuzhou Laboratory  \nNo.388, Ruoshui Street, SIP, Jiangsu 215123, China M. Song, Z. Wang  \nState Key Laboratory of P","cbCaivbp9goQMVV2","https://ap.wps.com/l/cbCaivbp9goQMVV2","pdf",8078017,1,10,"English","en",105,"# Introduction\n## Machine Learning-Enhanced APT for CSRO\n## Need for 3D Quantification\n# Methods and Background\n## Limitations of 2D Projection Imaging\n## Challenges in Identifying CSRO in APT","[{\"question\":\"What does chemical short-range order (CSRO) mean in this work?\",\"answer\":\"CSRO describes self-organization of specific atomic species on particular crystal sites in solids, often influencing mechanical and functional properties. However, quantitative links to CSRO domain morphology and configurations have been unclear.\"},{\"question\":\"How does the proposed ML-APT approach quantify CSRO in 3D?\",\"answer\":\"The work introduces a bottom-up machine learning approach that mines near-atomically resolved APT data. It provides 3D quantitative analysis of CSRO without requiring prior knowledge of CSRO configurations.\"},{\"question\":\"Why is 3D characterization of CSRO important compared with commonly used TEM methods?\",\"answer\":\"TEM-based approaches rely heavily on 2D projections, where observed features can be confounded by planar defects or higher-order diffraction effects. A reliable 3D perspective is needed to resolve contradictions and support materials design.\"}]","Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering | PDF",1785937607,25,{"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},"machine-learning-enabled-tomographic-imaging-of-chemical-short-range-atomic-ordering","",{"@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/machine-learning-enabled-tomographic-imaging-of-chemical-short-range-atomic-ordering/127221/",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","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does chemical short-range order (CSRO) mean in this work?","Question",{"text":76,"@type":77},"CSRO describes self-organization of specific atomic species on particular crystal sites in solids, often influencing mechanical and functional properties. However, quantitative links to CSRO domain morphology and configurations have been unclear.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed ML-APT approach quantify CSRO in 3D?",{"text":81,"@type":77},"The work introduces a bottom-up machine learning approach that mines near-atomically resolved APT data. It provides 3D quantitative analysis of CSRO without requiring prior knowledge of CSRO configurations.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is 3D characterization of CSRO important compared with commonly used TEM methods?",{"text":85,"@type":77},"TEM-based approaches rely heavily on 2D projections, where observed features can be confounded by planar defects or higher-order diffraction effects. A reliable 3D perspective is needed to resolve contradictions and support materials design.","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},[94,98,102,106,111,116,121,124,129,132,135],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]