[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127351-en":3,"doc-seo-127351-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127351,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction","Understanding the grain morphology, orientation distribution, and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction can map crystal orientation, yet resolution is limited when grain sizes are smaller than the beam, causing mixed Laue peaks. A physics-informed machine learning (PIML) framework combines a CNN feature extractor with physics-informed filtering to recover nanoscale grain mapping. Applied to Au nanocrystals, it resolves grain size, orientation distribution, and morphology from microdiffraction scans, matching electron backscatter diffraction results and generalizing to other diffraction-based probes.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nPhysics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction  \nPermalink  \n[https://escholarship.org/uc/item/2cp607c6](https://escholarship.org/uc/item/2cp607c6)  \nJournal  \nJournal of Applied Crystallography, 58(6)  \nAuthors  \nChan, Ka Hung  \nHuang, Xinyue Tamura, Nobumichi et al.  \nPublication Date  \n2025-12-01  \nDOI  \n10.1107/s160057672500826x  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial License, available at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n1 Physics-informed Machine Learning Analysis for  \n2 Nanoscale Grain Mapping by Synchrotron Laue  \n3 Microdiffraction  \n4 Ka Hung Chan 1,2 , Xinyue Huang 1 , Nobumichi Tamura2 , and Xian Chen† 1  \n5 a Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, 6 Hong Kong  \n7 b Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, United States  \n8 † [xianchen@ust.hk](xianchen@ust.hk)  \n9 Synopsis  \n10 Our work presents a physics-informed machine learning (PIML) approach that ad- 11 vances materials characterization by overcoming the fundamental resolution limits of  \n12 synchrotron X-ray Laue microdiffraction. By integrating deep learning with physical  \n13 constraints, we demonstrate that nanoscale grain mapping can be achieved even when  \n14 using micron-sized X-ray probes.  \n15 Abstract  \n16 Understanding the grain morphology, orientation distribution, and crystal structure of  \n17 nanocrystals is essential for optimizing the mechanical and physical properties of func- 18 tional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for  \n19 characterizing crystal structures and orientation mapping using focused X-rays. How- 20 ever, when grain sizes are smaller than the beam size, mixed peaks in the Laue pattern  \n21 from neighboring grains limit the resolution of grain morphology mapping. We propose  \n22 a physics-informed machine learning (PIML) approach that combines a Convolutional  \n23 Neural Network (CNN) feature extractor with a physics-informed filtering algorithm to  \n24 overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain  \n25 mapping. Our PIML method successfully resolves the grain size, orientation distribu- 26 tion, and morphology of Au nanocrystals through synchrotron microdiffraction scans, 27 showing good agreement with electron backscatter diffraction results. This PIML- 28 assisted synchrotron microdiffraction analysis can be generalized to other diffraction- 29 based probes, enabling the characterization of nanosized structures with micron-sized  \n30 probes.  \n31 Keywords: synchrotron Laue microdiffraction; physics-informed machine learning; nanocrys- 32 tals; X-ray crystallography  \n33 1 Introduction  \n34 Morphological configurations of polycrystals, including phase interfaces, grain boundaries, orienta- 35 tion distribution, and textures, play a profound role in the mechanical and physical properties of  \n36 functional materials. The nanoscale grains and precipitates underlie disruptive technological ad- 37 vancements such as solar cells (Wang et al. , 2023; Kodalle et al. , 2024), plasmonic devices (Zgrabik 38 & Hu, 2015; Bi et al. , 2021), and thermoelectric devices(Liu et al. , 2018; Ojo et al. , 2021) . A com- 39 prehensive analysis of the morphological features of grains and heterogeneous phases is necessary  \n40 for the development of novel materials with tailored properties. Laue microdiffraction experiments  \n41 are widely utilized to characterize crystallographic information, grain orientations, localized strains, 42 heterogeneous phases, and precipitates, but the","cbCaimnHJHT5qXsM","https://ap.wps.com/l/cbCaimnHJHT5qXsM","pdf",1808566,2,1,13,"English","en",105,"# Introduction\n## Grain morphology and the need for nanoscale characterization\n## Limits of Laue microdiffraction resolution\n## Physics-informed machine learning approach (PIML)\n# Keywords","[{\"question\":\"Why is grain mapping resolution limited in synchrotron Laue microdiffraction at the nanoscale?\",\"answer\":\"When grain sizes are smaller than the focused X-ray beam, the Laue pattern contains peaks diffracted from neighboring grains. This mixing limits the ability to resolve individual grain morphology and orientations.\"},{\"question\":\"What does the proposed physics-informed machine learning (PIML) method combine?\",\"answer\":\"The approach integrates a CNN feature extractor with a physics-informed filtering algorithm. This combination uses physical constraints to overcome spatial resolution limits of the X-ray probe.\"},{\"question\":\"How was the method validated in the study, and what results were achieved?\",\"answer\":\"The PIML-assisted microdiffraction analysis successfully resolved grain size, orientation distribution, and morphology of Au nanocrystals. Results showed good agreement with electron backscatter diffraction measurements.\"}]","Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction | PDF",1785938446,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"physics-informed-machine-learning-analysis-for-nanoscale-grain-mapping-by-synchrotron-laue-microdiffraction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/physics-informed-machine-learning-analysis-for-nanoscale-grain-mapping-by-synchrotron-laue-microdiffraction/127351/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","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},"Why is grain mapping resolution limited in synchrotron Laue microdiffraction at the nanoscale?","Question",{"text":76,"@type":77},"When grain sizes are smaller than the focused X-ray beam, the Laue pattern contains peaks diffracted from neighboring grains. This mixing limits the ability to resolve individual grain morphology and orientations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the proposed physics-informed machine learning (PIML) method combine?",{"text":81,"@type":77},"The approach integrates a CNN feature extractor with a physics-informed filtering algorithm. This combination uses physical constraints to overcome spatial resolution limits of the X-ray probe.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the method validated in the study, and what results were achieved?",{"text":85,"@type":77},"The PIML-assisted microdiffraction analysis successfully resolved grain size, orientation distribution, and morphology of Au nanocrystals. 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