Machine learning for nanoplasmonics

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

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