Color optimization of a core–shell nanoparticles layer using machine learning techniques

Autores
Urquia, Gonzalo Martin; Inchaussandague, Marina Elizabeth; Skigin, Diana Carina
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Neural networks were recently introduced in the field of nanophotonics as an alternative and powerful way to obtain the non-linear mapping between the geometry and composition of arbitrary nanophotonic structures on one hand, and their associated properties and functions on the other. Taking into account the recent advances in the application of the machine learning concept to the design of nanophotonic devices, we employ this tool for the optimization of photonic materials with specific color properties. We train a deep neural network (DNN) to solve the inverse problem, i.e., to obtain the geometrical parameters of the structure that best produce a desired reflected color. The analyzed system is a single layer of core–shell spheres composed of melanin and silica embedded in air, arranged in a hexagonal matrix. The network is trained using a dataset of the three CIE 1976 (L*a*b*) color coordinates obtained from the simulated reflectance spectra of a large set of structures. The direct problem is solved using the Korringa–Kohn–Rostoker method (KKR), widely applied to calculate the optical properties of sphere composites. The color optimization approach used in this work opens up new alternatives for the design of artificial photonic structures with tunable color effects.
Fil: Urquia, Gonzalo Martin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Grupo de Electromagnetismo Aplicado; Argentina
Fil: Inchaussandague, Marina Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Skigin, Diana Carina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Materia
MACHINE LEARNING
OPTIMIZATION
STRUCTURAL COLOR
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/224786
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Color optimization of a core–shell nanoparticles layer using machine learning techniquesUrquia, Gonzalo MartinInchaussandague, Marina ElizabethSkigin, Diana CarinaMACHINE LEARNINGOPTIMIZATIONSTRUCTURAL COLORhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Neural networks were recently introduced in the field of nanophotonics as an alternative and powerful way to obtain the non-linear mapping between the geometry and composition of arbitrary nanophotonic structures on one hand, and their associated properties and functions on the other. Taking into account the recent advances in the application of the machine learning concept to the design of nanophotonic devices, we employ this tool for the optimization of photonic materials with specific color properties. We train a deep neural network (DNN) to solve the inverse problem, i.e., to obtain the geometrical parameters of the structure that best produce a desired reflected color. The analyzed system is a single layer of core–shell spheres composed of melanin and silica embedded in air, arranged in a hexagonal matrix. The network is trained using a dataset of the three CIE 1976 (L*a*b*) color coordinates obtained from the simulated reflectance spectra of a large set of structures. The direct problem is solved using the Korringa–Kohn–Rostoker method (KKR), widely applied to calculate the optical properties of sphere composites. The color optimization approach used in this work opens up new alternatives for the design of artificial photonic structures with tunable color effects.Fil: Urquia, Gonzalo Martin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Grupo de Electromagnetismo Aplicado; ArgentinaFil: Inchaussandague, Marina Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Skigin, Diana Carina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaElsevier2023-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/224786Urquia, Gonzalo Martin; Inchaussandague, Marina Elizabeth; Skigin, Diana Carina; Color optimization of a core–shell nanoparticles layer using machine learning techniques; Elsevier; Results in Optics; 10; 2-2023; 1-72666-9501CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2666950122001237info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rio.2022.100334info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:59Zoai:ri.conicet.gov.ar:11336/224786instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:20:59.651CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Color optimization of a core–shell nanoparticles layer using machine learning techniques
title Color optimization of a core–shell nanoparticles layer using machine learning techniques
spellingShingle Color optimization of a core–shell nanoparticles layer using machine learning techniques
Urquia, Gonzalo Martin
MACHINE LEARNING
OPTIMIZATION
STRUCTURAL COLOR
title_short Color optimization of a core–shell nanoparticles layer using machine learning techniques
title_full Color optimization of a core–shell nanoparticles layer using machine learning techniques
title_fullStr Color optimization of a core–shell nanoparticles layer using machine learning techniques
title_full_unstemmed Color optimization of a core–shell nanoparticles layer using machine learning techniques
title_sort Color optimization of a core–shell nanoparticles layer using machine learning techniques
dc.creator.none.fl_str_mv Urquia, Gonzalo Martin
Inchaussandague, Marina Elizabeth
Skigin, Diana Carina
author Urquia, Gonzalo Martin
author_facet Urquia, Gonzalo Martin
Inchaussandague, Marina Elizabeth
Skigin, Diana Carina
author_role author
author2 Inchaussandague, Marina Elizabeth
Skigin, Diana Carina
author2_role author
author
dc.subject.none.fl_str_mv MACHINE LEARNING
OPTIMIZATION
STRUCTURAL COLOR
topic MACHINE LEARNING
OPTIMIZATION
STRUCTURAL COLOR
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Neural networks were recently introduced in the field of nanophotonics as an alternative and powerful way to obtain the non-linear mapping between the geometry and composition of arbitrary nanophotonic structures on one hand, and their associated properties and functions on the other. Taking into account the recent advances in the application of the machine learning concept to the design of nanophotonic devices, we employ this tool for the optimization of photonic materials with specific color properties. We train a deep neural network (DNN) to solve the inverse problem, i.e., to obtain the geometrical parameters of the structure that best produce a desired reflected color. The analyzed system is a single layer of core–shell spheres composed of melanin and silica embedded in air, arranged in a hexagonal matrix. The network is trained using a dataset of the three CIE 1976 (L*a*b*) color coordinates obtained from the simulated reflectance spectra of a large set of structures. The direct problem is solved using the Korringa–Kohn–Rostoker method (KKR), widely applied to calculate the optical properties of sphere composites. The color optimization approach used in this work opens up new alternatives for the design of artificial photonic structures with tunable color effects.
Fil: Urquia, Gonzalo Martin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Grupo de Electromagnetismo Aplicado; Argentina
Fil: Inchaussandague, Marina Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Skigin, Diana Carina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
description Neural networks were recently introduced in the field of nanophotonics as an alternative and powerful way to obtain the non-linear mapping between the geometry and composition of arbitrary nanophotonic structures on one hand, and their associated properties and functions on the other. Taking into account the recent advances in the application of the machine learning concept to the design of nanophotonic devices, we employ this tool for the optimization of photonic materials with specific color properties. We train a deep neural network (DNN) to solve the inverse problem, i.e., to obtain the geometrical parameters of the structure that best produce a desired reflected color. The analyzed system is a single layer of core–shell spheres composed of melanin and silica embedded in air, arranged in a hexagonal matrix. The network is trained using a dataset of the three CIE 1976 (L*a*b*) color coordinates obtained from the simulated reflectance spectra of a large set of structures. The direct problem is solved using the Korringa–Kohn–Rostoker method (KKR), widely applied to calculate the optical properties of sphere composites. The color optimization approach used in this work opens up new alternatives for the design of artificial photonic structures with tunable color effects.
publishDate 2023
dc.date.none.fl_str_mv 2023-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/224786
Urquia, Gonzalo Martin; Inchaussandague, Marina Elizabeth; Skigin, Diana Carina; Color optimization of a core–shell nanoparticles layer using machine learning techniques; Elsevier; Results in Optics; 10; 2-2023; 1-7
2666-9501
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224786
identifier_str_mv Urquia, Gonzalo Martin; Inchaussandague, Marina Elizabeth; Skigin, Diana Carina; Color optimization of a core–shell nanoparticles layer using machine learning techniques; Elsevier; Results in Optics; 10; 2-2023; 1-7
2666-9501
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2666950122001237
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rio.2022.100334
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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