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.

Details

Title
Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
Author
Shayeganfar Farzaneh 1 ; Shahsavari Rouzbeh 2 

 Rice University, Department of Civil and Environmental Engineering, Houston, USA (GRID:grid.21940.3e) (ISNI:0000 0004 1936 8278); Amirkabir University of Technology, Department of Physics and Energy Engineering, Tehran, Iran (GRID:grid.411368.9) (ISNI:0000 0004 0611 6995) 
 C-Crete Technologies LLC, Stafford, USA (GRID:grid.411368.9) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2554496874
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.