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This review summarizes machine-learning approaches that use closed-loop experimental data to optimize size, morphology, and other properties, and discusses recent case studies for condition-dependent nanostructures, concluding with key implementation challenges for ML-driven design in nanostructure synthesis.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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are nanostructure properties important in the Internet of NanoThings era?","Question",{"text":63,"@type":64},"Nanostructures enable application-specific functions in areas such as catalysis and energy, with their properties determined by controllable features like particle size, morphology, and structure.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What role do machine-learning methods play in nanostructure properties optimization?",{"text":68,"@type":64},"Machine-learning strategies use closed-loop experimental data from nanostructure synthesis to guide and optimize large sets of attributes related to size, morphology, and other properties from trained models.",{"name":70,"@type":61,"acceptedAnswer":71},"What major challenges are discussed for implementing ML data-driven design in nanostructure synthesis?",{"text":72,"@type":64},"The review highlights challenges in implementing ML-driven design for guiding synthesis and optimizing outcomes across the major constraints of data, modeling, and practical experimentation.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},125066,1785896445,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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Analysis with Boundary Elements  \njournal [homepage:](homepage: www.elsevier.com/locate/enganabound)[ www.elsevier.com/locate/enganabound](homepage: www.elsevier.com/locate/enganabound)  \n| Properties optimisation of nanostructures via machine learning: Progress and perspective |  |  |  |\n| --- | --- | --- | --- |\n| Nurul Akmal Che Laha,b,*\u003Cbr>a Centre for Advanced Intelligent Materials, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Tun Khalil Yaakob, Gambang, Pahang, 26300, Malaysia b Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, 26600, Malaysia |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Nanostructures Machine-learning Deep-learning Particle size Morphology |  | Nanostructures play a vast role in the current Internet of NanoThings (IoNT) era due to remarkable properties and features that precisely impart their desired application functions in catalysis, energy and other fields. The exploration in understanding their minute features caused by the flexibility of compositional and complex atomic arrangement from the synthesis reaction widely opens for the in-depth discovery of their search space such as particle size, morphology and structures that controlled the characteristics. A wide range of possible compositions and various lattice atomic arrangements combined with small particle size distribution and large surface area create grand challenges to copy/differentiate their corresponding specific properties. Thus, the employment of machine learning (ML)-based strategies using the closed-loop experimental data from the nanostructure synthesis to help navigate and optimise for the large classes of data attributes related to the size, morphology and other properties from the trained model are reviewed. The data attributes are assisted by discussions of the selected case studies from the recent literature that highlight different condition nanostructures. The review concludes with a discussion of perspectives on the major challenges in the implementation of ML data-driven design in the field of nanostructure synthesis. |  |\n\n1. Introduction  \nThe current demand on the Internet of NanoThings (IoNT) is to explore more research on the recent progress from supercomputers to smartphones, employing the best small bits of engineered nanomaterials to realise the miniaturised device [1–3]. All the recent nanotechnology advances (i.e., ultrathin designs, intrinsically stretchable nanomaterials and stretchable interconnection designs) involve the fabrication and integration of transdisciplinary modules that use highly customisable nanomaterials and designs [4–9]. For instance, patterned nanomaterials and nanocomposites are frequently utilised to enhance the electrochemically active surface site, which further increases the sensing sensitivity feature of the functional nanodevice. This is to say, the engineered nanomaterials are efficiently produced with tuning structure, composition and functionalisation via preferable bottom-up synthesis to yield unique output properties. Examples include the structural, optical, electrical, magnetic and chemical properties that rely on specific particle characteristics and surface modification from interfacial interaction during synthesis.  \nIt is crucial to have the ability to understand the assembly methodology process for the generation of nanomaterials that are not limited by standard environmental requirements. The operation of synthesised nanoengineered at elevated temperature, pressure and radiation conditions, especially in the future photonic applications is quite challenging. It requires an understanding of nanostructures’ behaviour under these extreme conditions. Their level of sensitivity, robustness, and unique property signatures are derived from both th","cbCaic2zxZnXJEzH","https://ap.wps.com/l/cbCaic2zxZnXJEzH","pdf",247178,"English","# Introduction\n# Machine-learning-based optimization framework\n## Closed-loop experimental data for navigation and optimisation\n# Case studies and condition-dependent nanostructures\n# Perspectives and major challenges","[{\"question\":\"Why are nanostructure properties important in the Internet of NanoThings era?\",\"answer\":\"Nanostructures enable application-specific functions in areas such as catalysis and energy, with their properties determined by controllable features like particle size, morphology, and structure.\"},{\"question\":\"What role do machine-learning methods play in nanostructure properties optimization?\",\"answer\":\"Machine-learning strategies use closed-loop experimental data from nanostructure synthesis to guide and optimize large sets of attributes related to size, morphology, and other properties from trained models.\"},{\"question\":\"What major challenges are discussed for implementing ML data-driven design in nanostructure synthesis?\",\"answer\":\"The review highlights challenges in implementing ML-driven design for guiding synthesis and optimizing outcomes across the major constraints of data, modeling, and practical experimentation.\"}]","Properties optimisation of nanostructures via machine learning - Progress and perspective | PDF"]