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Machine learning for nanoplasmonics.

Accepted version
Peer-reviewed

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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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Keywords

46 Information and Computing Sciences, 4611 Machine Learning

Journal Title

Nat Nanotechnol

Conference Name

Journal ISSN

1748-3387
1748-3395

Volume Title

Publisher

Springer Science and Business Media LLC
Sponsorship
MRC (MR/S017186/1)