[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121752-en":3,"doc-seo-121752-105":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},121752,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",6,"Technology","Machine-learning-driven accelerated design-method for meta-devices","Metasurface-based solar absorbers are widely used in green energy applications, yet conventional meta-absorber design often depends on expensive trial-and-error simulations to meet electromagnetic targets. This work proposes an accelerated design strategy using machine learning to reduce computation while improving efficiency. Decision Tree and Random Forest regressors are trained for forward and inverse configurations to generate required spectral responses, meta-atom shapes, and geometries. Reported MSE values quantify performance differences between forward and inverse modeling.","Machine-learning-driven accelerated design-method for meta-devices  \nIjaz, Sumbel; Noureen, Sadia; Rehman, Bacha; Aldaghri, Osamah; Cabrera, Humberto; Ibnaouf, Khalid H. ; Madkhali, Nawal; Mehmood, Muhammad Qasim  \nPublished in:  \nMaterials Today Communications  \nDOI:  \n10.1016/j.mtcomm.2023.106951  \nPublished: 01/09/2023  \nDocument Version  \nPeer reviewed version  \nLink to publication  \nCitation for pulished version (APA):  \nIjaz, S. , Noureen, S. , Rehman, B. , Aldaghri, O. , Cabrera, H. , Ibnaouf, K. H. , Madkhali, N. , & Mehmood, M. Q.(2023) . Machine-learning-driven accelerated design-method for meta-devices. Materials Today Communications, 37, 106951. [https://doi.org/10.1016/j.mtcomm.2023.106951](https://doi.org/10.1016/j.mtcomm.2023.106951)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• Unless given explicit permission, you may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 03. Aug. 2026  \nMachine-learning-driven Accelerated Design-method for Meta-devices  \nSumbel Ijaz1, Sadia Noureen1, Bacha Rehman2, Osamah Aldaghri3*, Humberto Cabrera4, Khalid H. Ibnaouf4, Nawal Madkhali4, Muhammad Qasim Mehmood 1*  \n1MicroNano Lab, Department of Electrical Engineering, Information Technology University (ITU) of the Punjab, Ferozepur Road, 54600 Lahore, Pakistan  \n2Faculty of Business, Law and Digital Technologies, Solent University, Southampton, UK  \n3Physics Department. College of Science, Imam Mohammed Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia  \n4  \nMLab, STI Unit, The Abdus Salam International Centre for Theoretical Physics, Strada Costiera 11, Trieste 34151, Italy  \n*[qasim.mehmood@itu.edu.pk](qasim.mehmood@itu.edu.pk), *[odaghri@imamu.edu.sa](odaghri@imamu.edu.sa).  \nAbstract:  \nMetasurface-based solar absorbers are widely acknowledged in green energy applications. The traditional roadmap for designing meta-absorbers relies on hefty trial-and-error computations for reaching design goals. In contrast, emerging machine-learning (ML) techniques can make such designs faster and more efficient, while conserving computational resources. ML models, i.e. Decision Tree (DT) and Random Forest (RF) Regressors are demonstrated in this work to design a variety of meta-absorbers. The models have been trained to generate desired electromagnetic spectrum, shapes, and geometries of meta-atoms during forward and inverse configurations, respectively. The MSE of DT and RF are 8.61×10−10 and 1.56×10−2 for forward, while 4.42×10−2 and 1.35×10−1 for inverse models, respectively.  \nKeywords:  \nMetasurface, Solar Absorber, Refractory materials, Machine Learning, Regression Analysis, Regressor, Decision Tree, Random Forest  \n1. Introduction  \nMetasurfaces – flat photonics (2D metamaterials) [1] – offer a flexible, multi-functional platform by controlling electromagnetic (EM) waves[2]–[4] propagating in the device at nanoscale[5]–[7] . They offer a variety of exciting properties[6], [8] based on engineering the geometry, material and dimensions of their building blocks i.e. the subwavelength “meta-atoms” [9], [10] . Electromagnetic resonances are induced in these nano-resonators to tailor the incident EM waves[11]–[13] . These characteristics have made such integrable meta-devices popular enough to find applications in a","cbCaioSgOtYNeFhI","https://ap.wps.com/l/cbCaioSgOtYNeFhI","pdf",5155908,1,15,"English","en",105,"# 1. Introduction\n## Metasurfaces and meta-atoms\n## Solar thermophotovoltaics and refractory materials\n## Problem with conventional design and motivation for ML","[{\"question\":\"为什么传统元吸收器设计需要大量试错计算？\",\"answer\":\"传统路线在达到目标设计效果时依赖较重的计算与迭代流程，因此计算成本高、效率受限。\"},{\"question\":\"本文采用了哪些机器学习模型来加速元器件设计？\",\"answer\":\"使用了决策树（DT）和随机森林（RF）回归器，并分别用于正向与反向配置下的设计生成。\"},{\"question\":\"正向与反向配置的性能如何评估？\",\"answer\":\"通过均方误差（MSE）衡量模型输出与目标之间的误差。文中给出了正向与反向模型对应的MSE结果，用于对比效果。\"}]","Machine-learning-driven accelerated design-method for meta-devices | PDF",1785806652,38,{"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-driven-accelerated-design-method-for-meta-devices","",{"@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-driven-accelerated-design-method-for-meta-devices/121752/",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","2026-08-04",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},"为什么传统元吸收器设计需要大量试错计算？","Question",{"text":75,"@type":76},"传统路线在达到目标设计效果时依赖较重的计算与迭代流程，因此计算成本高、效率受限。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本文采用了哪些机器学习模型来加速元器件设计？",{"text":80,"@type":76},"使用了决策树（DT）和随机森林（RF）回归器，并分别用于正向与反向配置下的设计生成。",{"name":82,"@type":73,"acceptedAnswer":83},"正向与反向配置的性能如何评估？",{"text":84,"@type":76},"通过均方误差（MSE）衡量模型输出与目标之间的误差。文中给出了正向与反向模型对应的MSE结果，用于对比效果。","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]