Artículo
Computer-aided design of polymeric materials: Computational study for characterization of databases for prediction of mechanical properties under polydispersity
Cravero, Fiorella
; Schustik, Santiago; Martínez, María Jimena
; Barranco, Carlos D.; Diaz, Monica Fatima
; Ponzoni, Ignacio
Fecha de publicación:
15/08/2019
Editorial:
Elsevier Science
Revista:
Chemometrics and Intelligent Laboratory Systems
ISSN:
0169-7439
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
In Polymer Informatics, quantitative structure-property relationship (QSPR) modeling is an emerging approach for predicting relevant properties of polymers in the context of computer-aided design of industrial materials. Nevertheless, most QSPR models available in the literature use simplistic computational representations of polymers based on their structural repetitive unit. The aim of this work is to evaluate the effect of this simplification and to analyze new strategies to achieve alternative characterizations that capture the phenomenon of polydispersity. In particular, the experiments reported in this work are focused on three mechanical properties derived from the tensile test. The reported results revealed the disadvantages of using these simplified representations. Besides, we contributed with alternative representations for the databases of polymer molecular descriptors that achieved more realistic and accurate QSPR models.
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Articulos (ICIC)
Articulos de INSTITUTO DE CS. E INGENIERIA DE LA COMPUTACION
Articulos de INSTITUTO DE CS. E INGENIERIA DE LA COMPUTACION
Articulos(PLAPIQUI)
Articulos de PLANTA PILOTO DE INGENIERIA QUIMICA (I)
Articulos de PLANTA PILOTO DE INGENIERIA QUIMICA (I)
Citación
Cravero, Fiorella; Schustik, Santiago; Martínez, María Jimena; Barranco, Carlos D.; Diaz, Monica Fatima; et al.; Computer-aided design of polymeric materials: Computational study for characterization of databases for prediction of mechanical properties under polydispersity; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 191; 15-8-2019; 65-72
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