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Plasmonic colours predicted by deep learning

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

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OriginalspracheEnglisch
Aufsatznummer8074
FachzeitschriftScientific reports
Jahrgang9
Ausgabenummer1
PublikationsstatusVeröffentlicht - 30 Mai 2019
Extern publiziertJa

Abstract

Picosecond laser pulses have been used as a surface colouring technique for noble metals, where the colours result from plasmonic resonances in the metallic nanoparticles created and redeposited on the surface by ablation and deposition processes. This technology provides two datasets which we use to train artificial neural networks, data from the experiment itself (laser parameters vs. colours) and data from the corresponding numerical simulations (geometric parameters vs. colours). We apply deep learning to predict the colour in both cases. We also propose a method for the solution of the inverse problem – wherein the geometric parameters and the laser parameters are predicted from colour – using an iterative multivariable inverse design method.

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Plasmonic colours predicted by deep learning. / Baxter, Joshua; Calà Lesina, Antonino; Guay, Jean Michel et al.
in: Scientific reports, Jahrgang 9, Nr. 1, 8074, 30.05.2019.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Baxter, J, Calà Lesina, A, Guay, JM, Weck, A, Berini, P & Ramunno, L 2019, 'Plasmonic colours predicted by deep learning', Scientific reports, Jg. 9, Nr. 1, 8074. https://doi.org/10.1038/s41598-019-44522-7
Baxter, J., Calà Lesina, A., Guay, J. M., Weck, A., Berini, P., & Ramunno, L. (2019). Plasmonic colours predicted by deep learning. Scientific reports, 9(1), Artikel 8074. https://doi.org/10.1038/s41598-019-44522-7
Baxter J, Calà Lesina A, Guay JM, Weck A, Berini P, Ramunno L. Plasmonic colours predicted by deep learning. Scientific reports. 2019 Mai 30;9(1):8074. doi: 10.1038/s41598-019-44522-7
Baxter, Joshua ; Calà Lesina, Antonino ; Guay, Jean Michel et al. / Plasmonic colours predicted by deep learning. in: Scientific reports. 2019 ; Jahrgang 9, Nr. 1.
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title = "Plasmonic colours predicted by deep learning",
abstract = "Picosecond laser pulses have been used as a surface colouring technique for noble metals, where the colours result from plasmonic resonances in the metallic nanoparticles created and redeposited on the surface by ablation and deposition processes. This technology provides two datasets which we use to train artificial neural networks, data from the experiment itself (laser parameters vs. colours) and data from the corresponding numerical simulations (geometric parameters vs. colours). We apply deep learning to predict the colour in both cases. We also propose a method for the solution of the inverse problem – wherein the geometric parameters and the laser parameters are predicted from colour – using an iterative multivariable inverse design method.",
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note = "Funding information: SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ldt., Ontario Centres of Excellence, Mitacs and Ontario academic member institutions. The authors thank SOSCIP for their computational resources and financial support. We acknowledge the computational resources and support from Scinet. We acknowledge financial support from the National Sciences and Engineering Research Council of Canada, and the Canada Research Chairs program. The authors also thank the Royal Canadian Mint for the use of their laser lab and the data acquired from it. We would like to thank Graham Killaire, Meagan Ginn, Guillaume C{\^o}t{\'e}, and Martin Charron for their contributions in creating the colour palettes at the Royal Canadian Mint.",
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AU - Berini, Pierre

AU - Ramunno, Lora

N1 - Funding information: SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ldt., Ontario Centres of Excellence, Mitacs and Ontario academic member institutions. The authors thank SOSCIP for their computational resources and financial support. We acknowledge the computational resources and support from Scinet. We acknowledge financial support from the National Sciences and Engineering Research Council of Canada, and the Canada Research Chairs program. The authors also thank the Royal Canadian Mint for the use of their laser lab and the data acquired from it. We would like to thank Graham Killaire, Meagan Ginn, Guillaume Côté, and Martin Charron for their contributions in creating the colour palettes at the Royal Canadian Mint.

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