[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118211-en":3,"doc-seo-118211-105":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},118211,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Convergence of Machine Learning with Microfluidics and Metamaterials to Build Smart Materials","Machine learning methods are transforming research by extracting hidden features of complex systems that are difficult to analyze using conventional approaches. This review focuses on smart material design by converging microfluidics, acoustic metamaterials, and machine learning. Microfluidics enables manipulation of fluids from microliter to femtoliter scales, yet analysis is difficult due to coupled chemical and biological processes. Acoustic metamaterials reshape acoustic waves, but their design is specialist-dependent and computationally expensive. The paper reviews limitations of microfluidics and acoustic metamaterials and shows how ML-based modeling and optimization can enable efficient, intelligent next-generation materials.","International Journal on Interactive Design and Manufacturing (IJIDeM) [https://doi.org/10.1007/s12008-023-01707-9](https://doi.org/10.1007/s12008-023-01707-9)  \nConvergence of machine learning with microfluidicsand metamaterials to build smart materials  \nPrateek Mittal1 · Krishnadas Narayanan Nampoothiri2 · Abhishek Jha2 · Shubhi Bansal1  \nReceived: 1 May 2023 / Accepted: 10 December 2023 © The Author(s) 2024  \nAbstract  \nRecent advances in machine learning have revolutionized numerous research domains by extracting the hidden features and properties of complex systems, which are not otherwise possible using conventional ways. One such development can be seen in designing smart materials, which intersects the ability of microfluidics and metamaterials with machine learning to achieve unprecedented abilities. Microfluidics involves generating and manipulating fluids in the form of liquid streams or droplets from microliter to femtoliter regimes. However, analysis of such fluid flows is always tiresome and challenging due to the complexity involved in the integration and detection of various chemical or biological processes. On the other hand, acoustic metamaterials manipulate acoustic waves to achieve unparalleled properties, which is not possible using natural materials. Nonetheless, the design of such metamaterials relies on the expertise of specialists or on analytical models that require an enormous number of expensive function evaluations, making this method extremely complex and time-consuming. These complexities and exorbitant function evaluations of both fluidic and metamaterial systems embark on the need for the support ofcomputational tools that can identify, process, and quantify the large amounts ofintricacy, thus machine learning techniques. This review discusses the shortcomings of microfluidics and acoustic metamaterials, which are overcome by neoteric machine learning approaches forbuilding smart materials. The following review endsbyproviding the importance and futureperspective of integrating machine learning and optimization approaches with microfluidic-based acoustic metamaterials to build smart and efficient intelligent next-generation materials.  \nKeywords Machine learning · Acoustic metamaterials · Microfluidics · Intelligent systems · Smart materials  \n1 Introduction  \nArtificial intelligence and machine learning methods have metamorphosed the existing research domainsand setup new pathways for researchers, academics, and industrialists to establish far-fetched research goals. These interdisciplinary tools have benefitted emerging research areas by exploring the complex phenomenon of existing systems and extracting the useful features based on the historic data values [1–3] .  \nB Prateek Mittal[prateek.mittal@ucl.ac.uk](prateek.mittal@ucl.ac.uk)  \nB Shubhi Bansal[shubhi.bansal@ucl.ac.uk](shubhi.bansal@ucl.ac.uk)  \n1 Department of Computer Science, University College London, London WC1E 6BT, UK  \n2 Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa, Vidyapeetham, Chennai 601103, India  \nMoreover, forecasting or predicting the future events/states is another paradigm achieved by integrating machine learning techniques in various systems [4, 5] . Leveraging the capability of machine learning algorithms to efficiently process the large data sets and surrogating the computationally expensive analysis with relatively cheap black-box models have led to the development of intelligent or smart systems to achieve superlative performance in contrast to conventional ways [6–8] . In this review, we present the development of smart systems focusing on two domains of microfluidics and acoustic metamaterials, which are recently been merged to develop next-generation fluid-based metamaterials. It has been observed that integrating the merits of fluidic systems and acoustic metamaterials with machine learning techniques resulted in the intelligent systems enabling their applicability in real-time for varie","cbCaiujsRRbfi8H3","https://ap.wps.com/l/cbCaiujsRRbfi8H3","pdf",906765,1,9,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"How does machine learning contribute to designing smart materials in this review?\",\"answer\":\"The review explains that machine learning can identify, process, and quantify complex behaviors by extracting features from data and using cheaper surrogate models instead of expensive analyses. This supports efficient smart material design across microfluidic and metamaterial systems.\"},{\"question\":\"What makes microfluidics analysis challenging?\",\"answer\":\"Microfluidics deals with manipulating fluids at very small scales, where integrating and detecting various chemical or biological processes makes flow analysis complex, time-consuming, and difficult.\"},{\"question\":\"Why is acoustic metamaterial design computationally demanding?\",\"answer\":\"Acoustic metamaterials require specialist expertise or analytical models and rely on a large number of expensive function evaluations. This complexity makes the design process extremely slow and resource-intensive.\"}]","Convergence of Machine Learning with Microfluidics and Metamaterials to Build Smart Materials | PDF",1785682248,23,{"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},"convergence-of-machine-learning-with-microfluidics-and-metamaterials-to-build-smart-materials","",{"@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/convergence-of-machine-learning-with-microfluidics-and-metamaterials-to-build-smart-materials/118211/",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","2026-08-02",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},"How does machine learning contribute to designing smart materials in this review?","Question",{"text":75,"@type":76},"The review explains that machine learning can identify, process, and quantify complex behaviors by extracting features from data and using cheaper surrogate models instead of expensive analyses. This supports efficient smart material design across microfluidic and metamaterial systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes microfluidics analysis challenging?",{"text":80,"@type":76},"Microfluidics deals with manipulating fluids at very small scales, where integrating and detecting various chemical or biological processes makes flow analysis complex, time-consuming, and difficult.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is acoustic metamaterial design computationally demanding?",{"text":84,"@type":76},"Acoustic metamaterials require specialist expertise or analytical models and rely on a large number of expensive function evaluations. 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