Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures

Autores
Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
New descriptors of main and side chains for polymers with high molecular weight are presented in order to predict the glass-transition temperature (Tg) by means of Tg/M ratio. They were obtained by molecular modeling for the middle unit in a series of three repeating units (trimer). Taken together with other classic descriptors calculated for the entire trimeric structure, the ones that correlated better with the property were selected by using a variable selection method. Only three descriptors were chosen: main chain surface area (SAMC), side chain mass (M SC) and number of rotatable bonds (RBN), where the first two descriptors belong to the set of the new ones proposed. By means of a multi-layer perceptron (MLP) neural network a good prediction model (R 2 = 0.953 and RMS = 0.25 K mol/g) was achieved and internally (R 2 = 0.964 and RMS = 0.41 K mol/g) and externally (R2 = 0.933 and RMS =0.47 K mol/g) validated. The dataset included 88 polymers. The selected descriptors and the quality of the obtained model demonstrate the advantages of capturing through computational molecular modeling the structural characteristics of the polymers' main and side chains in the prediction of Tg/M.
Fil: Palomba, Damián. 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
Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina
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
GLASS TRANSITION TEMPERATURE
MOLECULAR MODELING
STRUCTURE-PROPERTY RELATIONS
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:11336/54366
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/54366
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network_name_str CONICET Digital (CONICET)
spelling Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperaturesPalomba, DamiánVazquez, Gustavo EstebanDiaz, Monica FatimaGLASS TRANSITION TEMPERATUREMOLECULAR MODELINGSTRUCTURE-PROPERTY RELATIONShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1New descriptors of main and side chains for polymers with high molecular weight are presented in order to predict the glass-transition temperature (Tg) by means of Tg/M ratio. They were obtained by molecular modeling for the middle unit in a series of three repeating units (trimer). Taken together with other classic descriptors calculated for the entire trimeric structure, the ones that correlated better with the property were selected by using a variable selection method. Only three descriptors were chosen: main chain surface area (SAMC), side chain mass (M SC) and number of rotatable bonds (RBN), where the first two descriptors belong to the set of the new ones proposed. By means of a multi-layer perceptron (MLP) neural network a good prediction model (R 2 = 0.953 and RMS = 0.25 K mol/g) was achieved and internally (R 2 = 0.964 and RMS = 0.41 K mol/g) and externally (R2 = 0.933 and RMS =0.47 K mol/g) validated. The dataset included 88 polymers. The selected descriptors and the quality of the obtained model demonstrate the advantages of capturing through computational molecular modeling the structural characteristics of the polymers' main and side chains in the prediction of Tg/M.Fil: Palomba, Damián. 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; ArgentinaFil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; ArgentinaFil: 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 Science Inc2012-09info: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/11336/54366Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima; Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures; Elsevier Science Inc; Journal Of Molecular Graphics & Modelling; 38; 9-2012; 137-1471093-3263CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1093326312000435info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmgm.2012.04.006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:25Zoai:ri.conicet.gov.ar:11336/54366instacron: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 09:40:25.301CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
title Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
spellingShingle Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
Palomba, Damián
GLASS TRANSITION TEMPERATURE
MOLECULAR MODELING
STRUCTURE-PROPERTY RELATIONS
title_short Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
title_full Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
title_fullStr Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
title_full_unstemmed Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
title_sort Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
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 GLASS TRANSITION TEMPERATURE
MOLECULAR MODELING
STRUCTURE-PROPERTY RELATIONS
topic GLASS TRANSITION TEMPERATURE
MOLECULAR MODELING
STRUCTURE-PROPERTY RELATIONS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv New descriptors of main and side chains for polymers with high molecular weight are presented in order to predict the glass-transition temperature (Tg) by means of Tg/M ratio. They were obtained by molecular modeling for the middle unit in a series of three repeating units (trimer). Taken together with other classic descriptors calculated for the entire trimeric structure, the ones that correlated better with the property were selected by using a variable selection method. Only three descriptors were chosen: main chain surface area (SAMC), side chain mass (M SC) and number of rotatable bonds (RBN), where the first two descriptors belong to the set of the new ones proposed. By means of a multi-layer perceptron (MLP) neural network a good prediction model (R 2 = 0.953 and RMS = 0.25 K mol/g) was achieved and internally (R 2 = 0.964 and RMS = 0.41 K mol/g) and externally (R2 = 0.933 and RMS =0.47 K mol/g) validated. The dataset included 88 polymers. The selected descriptors and the quality of the obtained model demonstrate the advantages of capturing through computational molecular modeling the structural characteristics of the polymers' main and side chains in the prediction of Tg/M.
Fil: Palomba, Damián. 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
Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina
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 New descriptors of main and side chains for polymers with high molecular weight are presented in order to predict the glass-transition temperature (Tg) by means of Tg/M ratio. They were obtained by molecular modeling for the middle unit in a series of three repeating units (trimer). Taken together with other classic descriptors calculated for the entire trimeric structure, the ones that correlated better with the property were selected by using a variable selection method. Only three descriptors were chosen: main chain surface area (SAMC), side chain mass (M SC) and number of rotatable bonds (RBN), where the first two descriptors belong to the set of the new ones proposed. By means of a multi-layer perceptron (MLP) neural network a good prediction model (R 2 = 0.953 and RMS = 0.25 K mol/g) was achieved and internally (R 2 = 0.964 and RMS = 0.41 K mol/g) and externally (R2 = 0.933 and RMS =0.47 K mol/g) validated. The dataset included 88 polymers. The selected descriptors and the quality of the obtained model demonstrate the advantages of capturing through computational molecular modeling the structural characteristics of the polymers' main and side chains in the prediction of Tg/M.
publishDate 2012
dc.date.none.fl_str_mv 2012-09
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/11336/54366
Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima; Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures; Elsevier Science Inc; Journal Of Molecular Graphics & Modelling; 38; 9-2012; 137-147
1093-3263
CONICET Digital
CONICET
url http://hdl.handle.net/11336/54366
identifier_str_mv Palomba, Damián; Vazquez, Gustavo Esteban; Diaz, Monica Fatima; Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures; Elsevier Science Inc; Journal Of Molecular Graphics & Modelling; 38; 9-2012; 137-147
1093-3263
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/S1093326312000435
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmgm.2012.04.006
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 Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
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
_version_ 1844613278790057984
score 13.070432

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