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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/26340
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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 Técnicas2025-09-29T09:39:51Zoai: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:34982025-09-29 09:39:51.975CONICET 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 |
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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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13.070432 |