[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120692-en":3,"doc-seo-120692-105":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},120692,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",6,"Technology","Machine Learning for Mie-Tronics","Electromagnetic multipole expansion theory enables nanoscale light–matter interactions, especially in subwavelength dielectric meta-atoms, but traditional brute-force optimization over geometries and materials is costly and inefficient for complex designs. This study introduces machine learning for designing dielectric metaatoms with targeted multipolar moments up to octupole order. It builds forward prediction models linking scattering response to meta-atom topology and an inverse design model that reconstructs scatterers with desired moments, demonstrating tandem networks, higher-order magnetic response, super-scattering, and accurate electric-field prediction across broad spectra.","Machine Learning for Mie-Tronics  \nWenhao Li1, Hooman Barati Sedeh1, Willie J. Padilla1, Simiao Ren1,2, Jordan Malof2, Natalia M.  \nLitchinitser1, *  \n1 Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA 2 Department of Computer Science, University of Montana, Missoula, MT, USA  \n*[natalia.litchinitser@duke.edu](natalia.litchinitser@duke.edu)  \nAbstract  \nElectromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization methods, relying on the iterative exploration of various geometries and materials, are employed to obtain the desired multipolar moments, these approaches are computationally demanding and less effective for intricate designs. In this study, we unveil the potential of machine learning for designing dielectric metaatoms with desired multipolar moments up to the octupole terms. Specifically, we develop forward prediction models to unravel the intricate relationship between the scattering response and the topological attributes of individual meta-atoms, and an inverse design model to reconstruct scatterers with the targeted multipolar moments. Utilizing a tandem network trained to tailor dielectric meta-atoms for generating intended multipolar moments across a broad spectral range, we further demonstrate the generation of uniquely shaped meta-atoms for exciting exclusive higher order magnetic response and establishing super-scattering regime of light-matter interaction. We also illustrate the accurate prediction of electric field distributions within the given scatterer. Our versatile methodology can be readily applied to existing datasets and seamlessly integrated with various network architectures and problem domains, making it a valuable tool for the design of different platforms at nanoscale.  \nIntroduction  \nRapid progress in photonics and nanofabrication has opened up new prospects for realizing engineered scatterers to manipulate light at the nanoscale [1] . These subwavelength particles can be arranged in isolated, two-and threedimensional arrangements, known as meta-atoms [2], metasurfaces [3], and metamaterials [4], respectively, and have been shown to facilitate various applications such as beam steering [5], holography [6], nonlinear harmonic generation [7], and Kerker, anti-Kerker, and transverse Kerker effects [8], to name a few. While electromagnetic multipole expansion theory, a cornerstone of light-matter interactions, has facilitated the study of these intriguing optical phenomena, brute force optimization methods relying on an iterative exploration of various geometries and materials, are conventionally employed to obtain the desired response [9-13] . However, these approaches are computationally demanding and less effective for intricate designs, which leads to a fundamental trade-off between performance and time, highlighting the need for alternative methods that offer faster and more efficient solutions to overcome these limitations.  \nIn recent years, machine learning (ML) models have significantly evolved and lead to numerous breakthroughs in various domains such as finance [14], healthcare [15], computer vision [16], and robotics [17] . Followed by such a fruitful progress in this field of research, ML has recently emerged as a powerful tool in photonics for the design and analysis of various subwavelength platforms [18-29] . In particular, contrary to the conventional numerical approaches, ML methods not only offer fast prediction techniques that facilitate the optimization process within high-degree design spaces, but also serve as a valuable technique in tackling inverse design challenges by predicting nanostructure geometries that fulfill specific optical property criteria. Although there has been considerable effort dedicated to implementing ML for designing optical metasurface","cbCaietQwU90PzKv","https://ap.wps.com/l/cbCaietQwU90PzKv","pdf",1309593,1,19,"English","en",105,"# Introduction\n## Meta-atoms and engineered light manipulation\n## Multipole expansion and brute-force optimization limits\n## Machine learning in photonics and inverse design\n## Focus and novelty for isolated meta-atoms\n# Method Overview\n## Multipole-based physical connection between topology and fields\n## Forward prediction model (FPM)\n## Inverse design model (IDM)\n# Results and Demonstrations\n## Targeted multipolar moments up to octupole terms\n## Higher-order magnetic response and exclusive resonance generation\n## Super-scattering regime\n## Electric-field distribution prediction\n# Applications and Integration\n## Reuse with existing datasets\n## Compatibility with diverse network architectures and domains","[{\"question\":\"Why are brute-force optimization methods inefficient for meta-atom multipole design?\",\"answer\":\"Brute-force approaches iteratively explore many geometries and materials, making computation expensive. They also perform worse for intricate designs, creating a trade-off between time and achievable performance.\"},{\"question\":\"What do the forward prediction models learn in this study?\",\"answer\":\"The forward prediction models decode the relationship between a meta-atom’s topological attributes and its scattering response, using multipole expansion as the physical basis.\"},{\"question\":\"How does the inverse design model help generate meta-atoms with desired multipolar moments?\",\"answer\":\"The inverse design model reconstructs the scatterer geometry that targets specific multipolar moments, enabling generation of uniquely shaped dielectric meta-atoms across a broad spectral range.\"}]","Machine Learning for Mie-Tronics | PDF",1785731568,48,{"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},"machine-learning-for-mie-tronics","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-mie-tronics/120692/",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 are brute-force optimization methods inefficient for meta-atom multipole design?","Question",{"text":75,"@type":76},"Brute-force approaches iteratively explore many geometries and materials, making computation expensive. They also perform worse for intricate designs, creating a trade-off between time and achievable performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What do the forward prediction models learn in this study?",{"text":80,"@type":76},"The forward prediction models decode the relationship between a meta-atom’s topological attributes and its scattering response, using multipole expansion as the physical basis.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the inverse design model help generate meta-atoms with desired multipolar moments?",{"text":84,"@type":76},"The inverse design model reconstructs the scatterer geometry that targets specific multipolar moments, enabling generation of uniquely shaped dielectric meta-atoms across a broad spectral range.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]