Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory

Glass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimenta...

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Autores: Borredon, Claudia, Miccio, Luis A., Phan, Anh D., Schwartz, Gustavo A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/284395
Acceso en línea:http://hdl.handle.net/10261/284395
Access Level:acceso abierto
Palabra clave:QSPR
Properties prediction
Artificial neural networks
ECNLE
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spelling Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theoryBorredon, ClaudiaMiccio, Luis A.Phan, Anh D.Schwartz, Gustavo A.QSPRProperties predictionArtificial neural networksECNLEGlass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimentally accessible after synthesizing it, implying a time-consuming and costly process. In this work, we propose combining artificial neural networks and disordered systems theory to estimate the glass transition temperature and the temperature dependence of the main relaxation time based on the knowledge of the molecule's chemical structure. This approach provides a way to assess the dynamics of molecular glass formers, with reasonable accuracy, even before synthesizing them. We expect this methodology to boost industrial development, save time and resources, and accelerate the scientific understanding of structure-properties relationships.We gratefully acknowledge the financial support from the Spanish Government “Ministerio de Ciencia e Innovación" (PID2019-104650GB-C21) and the Basque Government (IT-1566-22). This research was also funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 103.01-2019.318. We also acknowledge the support of NVIDIA Corporation with the donation of two GPUs used for this research.We also acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer reviewedElsevierMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Eusko JaurlaritzaCSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)National Foundation for Science and Technology Development (Vietnam)NVIDIA CorporationConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/284395reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104650GB-C21https://doi.org/10.1016/j.nocx.2022.100106Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2843952026-05-22T06:33:51Z
dc.title.none.fl_str_mv Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
title Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
spellingShingle Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
Borredon, Claudia
QSPR
Properties prediction
Artificial neural networks
ECNLE
title_short Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
title_full Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
title_fullStr Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
title_full_unstemmed Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
title_sort Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory
dc.creator.none.fl_str_mv Borredon, Claudia
Miccio, Luis A.
Phan, Anh D.
Schwartz, Gustavo A.
author Borredon, Claudia
author_facet Borredon, Claudia
Miccio, Luis A.
Phan, Anh D.
Schwartz, Gustavo A.
author_role author
author2 Miccio, Luis A.
Phan, Anh D.
Schwartz, Gustavo A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Eusko Jaurlaritza
CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)
National Foundation for Science and Technology Development (Vietnam)
NVIDIA Corporation
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv QSPR
Properties prediction
Artificial neural networks
ECNLE
topic QSPR
Properties prediction
Artificial neural networks
ECNLE
description Glass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimentally accessible after synthesizing it, implying a time-consuming and costly process. In this work, we propose combining artificial neural networks and disordered systems theory to estimate the glass transition temperature and the temperature dependence of the main relaxation time based on the knowledge of the molecule's chemical structure. This approach provides a way to assess the dynamics of molecular glass formers, with reasonable accuracy, even before synthesizing them. We expect this methodology to boost industrial development, save time and resources, and accelerate the scientific understanding of structure-properties relationships.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/284395
url http://hdl.handle.net/10261/284395
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104650GB-C21
https://doi.org/10.1016/j.nocx.2022.100106
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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