Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break

Autores
Cravero, Fiorella; Diaz, Monica Fatima; Ponzoni, Ignacio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The artificial intelligence-based prediction of the mechanical properties derived from the tensile test, plays a key role in assessing the application profile of new polymeric materials, specifically in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.
Fil: Cravero, Fiorella. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Diaz, Monica Fatima. Universidad Nacional del Sur. Departamento de Ingeniería Química; 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: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
Materia
MACHINE LEARNING
VISUAL ANALYTICS
POLYMER INFORMATICS
QSPR
MECHANICAL PROPERTIES
STRENGTH AT BREAK
TENSILE TEST
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:11336/162419
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/162419
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at breakCravero, FiorellaDiaz, Monica FatimaPonzoni, IgnacioMACHINE LEARNINGVISUAL ANALYTICSPOLYMER INFORMATICSQSPRMECHANICAL PROPERTIESSTRENGTH AT BREAKTENSILE TESThttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The artificial intelligence-based prediction of the mechanical properties derived from the tensile test, plays a key role in assessing the application profile of new polymeric materials, specifically in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.Fil: Cravero, Fiorella. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Diaz, Monica Fatima. Universidad Nacional del Sur. Departamento de Ingeniería Química; 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: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaAmerican Institute of Physics2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/162419Cravero, Fiorella; Diaz, Monica Fatima; Ponzoni, Ignacio; Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break; American Institute of Physics; Journal of Chemical Physics; 156; 20; 5-2022; 1-310021-9606CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://aip.scitation.org/doi/10.1063/5.0087392info:eu-repo/semantics/altIdentifier/doi/10.1063/5.0087392info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:24:56Zoai:ri.conicet.gov.ar:11336/162419instacron: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 10:24:57.116CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
title Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
spellingShingle Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
Cravero, Fiorella
MACHINE LEARNING
VISUAL ANALYTICS
POLYMER INFORMATICS
QSPR
MECHANICAL PROPERTIES
STRENGTH AT BREAK
TENSILE TEST
title_short Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
title_full Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
title_fullStr Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
title_full_unstemmed Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
title_sort Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
dc.creator.none.fl_str_mv Cravero, Fiorella
Diaz, Monica Fatima
Ponzoni, Ignacio
author Cravero, Fiorella
author_facet Cravero, Fiorella
Diaz, Monica Fatima
Ponzoni, Ignacio
author_role author
author2 Diaz, Monica Fatima
Ponzoni, Ignacio
author2_role author
author
dc.subject.none.fl_str_mv MACHINE LEARNING
VISUAL ANALYTICS
POLYMER INFORMATICS
QSPR
MECHANICAL PROPERTIES
STRENGTH AT BREAK
TENSILE TEST
topic MACHINE LEARNING
VISUAL ANALYTICS
POLYMER INFORMATICS
QSPR
MECHANICAL PROPERTIES
STRENGTH AT BREAK
TENSILE TEST
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The artificial intelligence-based prediction of the mechanical properties derived from the tensile test, plays a key role in assessing the application profile of new polymeric materials, specifically in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.
Fil: Cravero, Fiorella. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Diaz, Monica Fatima. Universidad Nacional del Sur. Departamento de Ingeniería Química; 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: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
description The artificial intelligence-based prediction of the mechanical properties derived from the tensile test, plays a key role in assessing the application profile of new polymeric materials, specifically in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
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/11336/162419
Cravero, Fiorella; Diaz, Monica Fatima; Ponzoni, Ignacio; Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break; American Institute of Physics; Journal of Chemical Physics; 156; 20; 5-2022; 1-31
0021-9606
CONICET Digital
CONICET
url http://hdl.handle.net/11336/162419
identifier_str_mv Cravero, Fiorella; Diaz, Monica Fatima; Ponzoni, Ignacio; Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break; American Institute of Physics; Journal of Chemical Physics; 156; 20; 5-2022; 1-31
0021-9606
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://aip.scitation.org/doi/10.1063/5.0087392
info:eu-repo/semantics/altIdentifier/doi/10.1063/5.0087392
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 American Institute of Physics
publisher.none.fl_str_mv American Institute of Physics
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_ 1844614246911967232
score 13.070432

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