
Machine learning for nanoplasmonics
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ABSTRACT Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical
and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific
nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine
learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and
theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic
conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in
nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data. Access through your institution Buy or subscribe This
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MACHINE LEARNING Article 13 July 2021 TWO-STEP MACHINE LEARNING ENABLES OPTIMIZED NANOPARTICLE SYNTHESIS Article Open access 20 April 2021 A ROBOTIC PLATFORM FOR THE SYNTHESIS OF COLLOIDAL
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Natural Science and Engineering Research Council of Canada, The Royal Society, UK, International Exchange Scheme IES\R3\203092 and UKRI Future Leaders Fellowship programme, grant number
MR/S017186/1. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Département de chimie, Quebec Center for Advanced Materials, Regroupement québécois sur les matériaux de pointe, and Centre
interdisciplinaire de recherche sur le cerveau et l’apprentissage, Université de Montréal, Montréal, Quebec, Canada Jean-Francois Masson * Engineering Department, University of Cambridge,
Cambridge, UK John S. Biggins * Department of Material Science and Metallurgy, University of Cambridge, Cambridge, UK Emilie Ringe * Department of Earth Science, University of Cambridge,
Cambridge, UK Emilie Ringe Authors * Jean-Francois Masson View author publications You can also search for this author inPubMed Google Scholar * John S. Biggins View author publications You
can also search for this author inPubMed Google Scholar * Emilie Ringe View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHORS
Correspondence to Jean-Francois Masson, John S. Biggins or Emilie Ringe. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW
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E. Machine learning for nanoplasmonics. _Nat. Nanotechnol._ 18, 111–123 (2023). https://doi.org/10.1038/s41565-022-01284-0 Download citation * Received: 08 March 2022 * Accepted: 27 October
2022 * Published: 26 January 2023 * Issue Date: February 2023 * DOI: https://doi.org/10.1038/s41565-022-01284-0 SHARE THIS ARTICLE Anyone you share the following link with will be able to
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