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Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation

The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is...

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Main Authors: Luis A. Miccio, Claudia, Borredon, Ulises, Casado
Format: Bài trích
Language:English
Published: MDPI 2022
Subjects:
QSPR
Dynamics prediction
Online Access:https://www.mdpi.com/2073-4360/14/8/1573
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5945
https://doi.org/10.3390/polym14081573
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spelling oai:localhost:PNK-59452022-08-17T05:54:50Z Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation Luis A. Miccio Claudia, Borredon Ulises, Casado QSPR Dynamics prediction The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time-consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer 2022-07-13T02:00:02Z 2022-07-13T02:00:02Z 2022 Bài trích https://www.mdpi.com/2073-4360/14/8/1573 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5945 https://doi.org/10.3390/polym14081573 en MDPI
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic QSPR
Dynamics prediction
spellingShingle QSPR
Dynamics prediction
Luis A. Miccio
Claudia, Borredon
Ulises, Casado
Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
description The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time-consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer
format Bài trích
author Luis A. Miccio
Claudia, Borredon
Ulises, Casado
author_facet Luis A. Miccio
Claudia, Borredon
Ulises, Casado
author_sort Luis A. Miccio
title Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_short Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_full Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_fullStr Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_full_unstemmed Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation
title_sort approaching polymer dynamics combining artificial neural networks and elastically collective nonlinear langevin equation
publisher MDPI
publishDate 2022
url https://www.mdpi.com/2073-4360/14/8/1573
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5945
https://doi.org/10.3390/polym14081573
_version_ 1751856318809899008
score 8.8894005

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