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Machine learning for nanoplasmonics

Nature Nanotechnology volume 18, pages 111–123 (2023)Cite this article

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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.

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Fig. 1: Traditional workflow in nanoplasmonics, in which practitioners make nanostructures, characterize their structure and plasmonic properties, and then optimize for application.
Fig. 2: ML-enhanced prediction and characterization.
Fig. 3: ML-controlled synthesis.
Fig. 4: ML extraction of information from images.
Fig. 5: Applications of ML in sensing.

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Acknowledgements

We acknowledge the financial support of the 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

  1. 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

  2. Engineering Department, University of Cambridge, Cambridge, UK

    John S. Biggins

  3. Department of Material Science and Metallurgy, University of Cambridge, Cambridge, UK

    Emilie Ringe

  4. Department of Earth Science, University of Cambridge, Cambridge, UK

    Emilie Ringe

Authors
  1. Jean-Francois Masson
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  2. John S. Biggins
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  3. Emilie Ringe
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Correspondence to Jean-Francois Masson, John S. Biggins or Emilie Ringe.

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Nature Nanotechnology thanks Regina Ragan and Xiaonan Wang for their contribution to the peer review of this work.

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Masson, JF., Biggins, J.S. & Ringe, E. Machine learning for nanoplasmonics. Nat. Nanotechnol. 18, 111–123 (2023). https://doi.org/10.1038/s41565-022-01284-0

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