Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites

Abstract Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO2) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the...

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Autores principales:	Farzaneh Shayeganfar, Rouzbeh Shahsavari
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dad9747d41ba4f3296187aab16b4af43
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spelling	oai:doaj.org-article:dad9747d41ba4f3296187aab16b4af432021-12-02T16:17:28ZDeep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites10.1038/s41598-021-94085-92045-2322https://doaj.org/article/dad9747d41ba4f3296187aab16b4af432021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94085-9https://doaj.org/toc/2045-2322Abstract Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO2) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the key to bottom-up design of molecular devices. We develop an integrated multidisciplinary approach based on electronic structure computation [density functional theory (DFT)] and big data mining [machine learning (ML)] in parallel with neural network (NN) and statistical analysis (SA) to design hybrid polymers from assembly on substrate. Here we demonstrate that interfacial pressure and structural deformation of polymer network adsorbed on GE and SiO2 offer unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks. Our findings serve as the platform for designing a wide range of typical inorganic heterostructures, involving noncovalent intermolecular interaction observed in many nanoscale electronic devices.Farzaneh ShayeganfarRouzbeh ShahsavariNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Farzaneh Shayeganfar Rouzbeh Shahsavari Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
description	Abstract Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO2) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the key to bottom-up design of molecular devices. We develop an integrated multidisciplinary approach based on electronic structure computation [density functional theory (DFT)] and big data mining [machine learning (ML)] in parallel with neural network (NN) and statistical analysis (SA) to design hybrid polymers from assembly on substrate. Here we demonstrate that interfacial pressure and structural deformation of polymer network adsorbed on GE and SiO2 offer unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks. Our findings serve as the platform for designing a wide range of typical inorganic heterostructures, involving noncovalent intermolecular interaction observed in many nanoscale electronic devices.
format	article
author	Farzaneh Shayeganfar Rouzbeh Shahsavari
author_facet	Farzaneh Shayeganfar Rouzbeh Shahsavari
author_sort	Farzaneh Shayeganfar
title	Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
title_short	Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
title_full	Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
title_fullStr	Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
title_full_unstemmed	Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
title_sort	deep learning method to accelerate discovery of hybrid polymer-graphene composites
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dad9747d41ba4f3296187aab16b4af43
work_keys_str_mv	AT farzanehshayeganfar deeplearningmethodtoacceleratediscoveryofhybridpolymergraphenecomposites AT rouzbehshahsavari deeplearningmethodtoacceleratediscoveryofhybridpolymergraphenecomposites
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Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites

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