Prediction of elongation at break for linear polymers

Autores
Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper we present results on prediction of elongation at break (target property) for a group of 77 amorphous polymers of high molecular weight. Novel descriptors are proposed in order to better represent structural features related to the target property. These proposed descriptors along with the classic ones, were calculated for the set of polymers. The final descriptors of the predictive model were obtained by using a combination of variable selection method and domain knowledge. The model consisted of three descriptors: Cross-head Speed (CHS), Number Average Molecular Weight/Main Chain Surface Area ratio (Mn/SAMC), and Normalized Main Chain Mass (nMMC). By means of a multi-layer perceptron (MLP) neural network a good prediction model (R2 = 0.88 and MAE = 1.89) was achieved, which was internally and externally validated. The model shows the advantages of using well-known parameters in the field of polymers and of capturing the structural characteristics of the main and side chains. Thus, more intelligent tools are developed for the design of new materials with a specific application profile.
Fil: Palomba, Damián. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Vazquez, Gustavo Esteban. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Universidad católica del Uruguay. Facultad de Ingeniería y Tecnologías; Uruguay
Fil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina
Materia
Quantitative Structure-Property Relationships
Molecular Modeling
Elongation at Break
Mechanical Properties
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:11336/26340
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Prediction of elongation at break for linear polymersPalomba, DamiánVazquez, Gustavo EstebanDiaz, Monica FatimaQuantitative Structure-Property RelationshipsMolecular ModelingElongation at BreakMechanical Propertieshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this paper we present results on prediction of elongation at break (target property) for a group of 77 amorphous polymers of high molecular weight. Novel descriptors are proposed in order to better represent structural features related to the target property. These proposed descriptors along with the classic ones, were calculated for the set of polymers. The final descriptors of the predictive model were obtained by using a combination of variable selection method and domain knowledge. The model consisted of three descriptors: Cross-head Speed (CHS), Number Average Molecular Weight/Main Chain Surface Area ratio (Mn/SAMC), and Normalized Main Chain Mass (nMMC). By means of a multi-layer perceptron (MLP) neural network a good prediction model (R2 = 0.88 and MAE = 1.89) was achieved, which was internally and externally validated. The model shows the advantages of using well-known parameters in the field of polymers and of capturing the structural characteristics of the main and side chains. Thus, more intelligent tools are developed for the design of new materials with a specific application profile.Fil: Palomba, Damián. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Vazquez, Gustavo Esteban. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Universidad católica del Uruguay. Facultad de Ingeniería y Tecnologías; UruguayFil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; ArgentinaElsevier Science2014-09-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/26340Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima; Prediction of elongation at break for linear polymers; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 139; 26-9-2014; 121-1310169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743914001981info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2014.09.009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:08:16Zoai:ri.conicet.gov.ar:11336/26340instacron: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:34982025-10-22 11:08:16.575CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Prediction of elongation at break for linear polymers
title Prediction of elongation at break for linear polymers
spellingShingle Prediction of elongation at break for linear polymers
Palomba, Damián
Quantitative Structure-Property Relationships
Molecular Modeling
Elongation at Break
Mechanical Properties
title_short Prediction of elongation at break for linear polymers
title_full Prediction of elongation at break for linear polymers
title_fullStr Prediction of elongation at break for linear polymers
title_full_unstemmed Prediction of elongation at break for linear polymers
title_sort Prediction of elongation at break for linear polymers
dc.creator.none.fl_str_mv Palomba, Damián
Vazquez, Gustavo Esteban
Diaz, Monica Fatima
author Palomba, Damián
author_facet Palomba, Damián
Vazquez, Gustavo Esteban
Diaz, Monica Fatima
author_role author
author2 Vazquez, Gustavo Esteban
Diaz, Monica Fatima
author2_role author
author
dc.subject.none.fl_str_mv Quantitative Structure-Property Relationships
Molecular Modeling
Elongation at Break
Mechanical Properties
topic Quantitative Structure-Property Relationships
Molecular Modeling
Elongation at Break
Mechanical Properties
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper we present results on prediction of elongation at break (target property) for a group of 77 amorphous polymers of high molecular weight. Novel descriptors are proposed in order to better represent structural features related to the target property. These proposed descriptors along with the classic ones, were calculated for the set of polymers. The final descriptors of the predictive model were obtained by using a combination of variable selection method and domain knowledge. The model consisted of three descriptors: Cross-head Speed (CHS), Number Average Molecular Weight/Main Chain Surface Area ratio (Mn/SAMC), and Normalized Main Chain Mass (nMMC). By means of a multi-layer perceptron (MLP) neural network a good prediction model (R2 = 0.88 and MAE = 1.89) was achieved, which was internally and externally validated. The model shows the advantages of using well-known parameters in the field of polymers and of capturing the structural characteristics of the main and side chains. Thus, more intelligent tools are developed for the design of new materials with a specific application profile.
Fil: Palomba, Damián. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Vazquez, Gustavo Esteban. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Universidad católica del Uruguay. Facultad de Ingeniería y Tecnologías; Uruguay
Fil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina
description In this paper we present results on prediction of elongation at break (target property) for a group of 77 amorphous polymers of high molecular weight. Novel descriptors are proposed in order to better represent structural features related to the target property. These proposed descriptors along with the classic ones, were calculated for the set of polymers. The final descriptors of the predictive model were obtained by using a combination of variable selection method and domain knowledge. The model consisted of three descriptors: Cross-head Speed (CHS), Number Average Molecular Weight/Main Chain Surface Area ratio (Mn/SAMC), and Normalized Main Chain Mass (nMMC). By means of a multi-layer perceptron (MLP) neural network a good prediction model (R2 = 0.88 and MAE = 1.89) was achieved, which was internally and externally validated. The model shows the advantages of using well-known parameters in the field of polymers and of capturing the structural characteristics of the main and side chains. Thus, more intelligent tools are developed for the design of new materials with a specific application profile.
publishDate 2014
dc.date.none.fl_str_mv 2014-09-26
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/11336/26340
Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima; Prediction of elongation at break for linear polymers; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 139; 26-9-2014; 121-131
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/26340
identifier_str_mv Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima; Prediction of elongation at break for linear polymers; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 139; 26-9-2014; 121-131
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743914001981
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2014.09.009
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
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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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