Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation

Autores
Miccio, Luis Alejandro; Borredon, Claudia; Casado, Ulises Martín; Phan, Anh D.; Schwartz, Gustavo Ariel
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time‐consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer.
Fil: Miccio, Luis Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina. Consejo Superior de Investigaciones Científicas; España. Universidad del País Vasco; España
Fil: Borredon, Claudia. Universidad del País Vasco; España. Consejo Superior de Investigaciones Científicas; España
Fil: Casado, Ulises Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Phan, Anh D.. Phenikaa University; Vietnam
Fil: Schwartz, Gustavo Ariel. Donostia International Phisycs Center; España. Universidad del País Vasco; España. Consejo Superior de Investigaciones Científicas; España
Materia
ARTIFICIAL NEURAL NETWORKS
DYNAMICS PREDICTION
POLYMERS
QSPR
SMART DESIGN
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/214023
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin EquationMiccio, Luis AlejandroBorredon, ClaudiaCasado, Ulises MartínPhan, Anh D.Schwartz, Gustavo ArielARTIFICIAL NEURAL NETWORKSDYNAMICS PREDICTIONPOLYMERSQSPRSMART DESIGNhttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time‐consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer.Fil: Miccio, Luis Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina. Consejo Superior de Investigaciones Científicas; España. Universidad del País Vasco; EspañaFil: Borredon, Claudia. Universidad del País Vasco; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Casado, Ulises Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Phan, Anh D.. Phenikaa University; VietnamFil: Schwartz, Gustavo Ariel. Donostia International Phisycs Center; España. Universidad del País Vasco; España. Consejo Superior de Investigaciones Científicas; EspañaMultidisciplinary Digital Publishing Institute2022-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/214023Miccio, Luis Alejandro; Borredon, Claudia; Casado, Ulises Martín; Phan, Anh D.; Schwartz, Gustavo Ariel; Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation; Multidisciplinary Digital Publishing Institute; Polymers; 14; 8; 4-2022; 1-122073-4360CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/polym14081573info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-4360/14/8/1573info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:44Zoai:ri.conicet.gov.ar:11336/214023instacron: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 09:38:45.004CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
spellingShingle Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
Miccio, Luis Alejandro
ARTIFICIAL NEURAL NETWORKS
DYNAMICS PREDICTION
POLYMERS
QSPR
SMART DESIGN
title_short Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_full Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_fullStr Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_full_unstemmed Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_sort Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
dc.creator.none.fl_str_mv Miccio, Luis Alejandro
Borredon, Claudia
Casado, Ulises Martín
Phan, Anh D.
Schwartz, Gustavo Ariel
author Miccio, Luis Alejandro
author_facet Miccio, Luis Alejandro
Borredon, Claudia
Casado, Ulises Martín
Phan, Anh D.
Schwartz, Gustavo Ariel
author_role author
author2 Borredon, Claudia
Casado, Ulises Martín
Phan, Anh D.
Schwartz, Gustavo Ariel
author2_role author
author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORKS
DYNAMICS PREDICTION
POLYMERS
QSPR
SMART DESIGN
topic ARTIFICIAL NEURAL NETWORKS
DYNAMICS PREDICTION
POLYMERS
QSPR
SMART DESIGN
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time‐consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer.
Fil: Miccio, Luis Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina. Consejo Superior de Investigaciones Científicas; España. Universidad del País Vasco; España
Fil: Borredon, Claudia. Universidad del País Vasco; España. Consejo Superior de Investigaciones Científicas; España
Fil: Casado, Ulises Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Phan, Anh D.. Phenikaa University; Vietnam
Fil: Schwartz, Gustavo Ariel. Donostia International Phisycs Center; España. Universidad del País Vasco; España. Consejo Superior de Investigaciones Científicas; España
description The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time‐consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer.
publishDate 2022
dc.date.none.fl_str_mv 2022-04
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/11336/214023
Miccio, Luis Alejandro; Borredon, Claudia; Casado, Ulises Martín; Phan, Anh D.; Schwartz, Gustavo Ariel; Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation; Multidisciplinary Digital Publishing Institute; Polymers; 14; 8; 4-2022; 1-12
2073-4360
CONICET Digital
CONICET
url http://hdl.handle.net/11336/214023
identifier_str_mv Miccio, Luis Alejandro; Borredon, Claudia; Casado, Ulises Martín; Phan, Anh D.; Schwartz, Gustavo Ariel; Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation; Multidisciplinary Digital Publishing Institute; Polymers; 14; 8; 4-2022; 1-12
2073-4360
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/polym14081573
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-4360/14/8/1573
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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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