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

Farzaneh Shayeganfar,Rouzbeh Shahsavari

doi:10.1038/s41598-021-94085-9

Farzaneh Shayeganfar, Rouzbeh Shahsavari

Open Access

https://doi.org/10.1038/s41598-021-94085-9

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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.

Highlights

Our demonstration of emergent computational approach that uses Neural network (NN), ML and Correlation matrix (CM) algorithms trained on density functional theory (DFT) big and deep data illustrates a path for developing new materials with exclusive physical and chemical properties that would be difficult to achieve through experimental set up
To study the structural properties of polymer adsorbed on GE and SiO2, we have used the periodic density functional theory (DFT) technique that employs localized atomic orbital basis functions implication in SIESTA p ackages[32]
The Brillouin zone (BZ) sampling is performed within the Monkhorst–Pack[35] by a fine grid of 12 × 12 × 1 to produce an accurate band structure

Summary

Introduction

Other interfacial properties for CP/SiO2 (see SI for more details) play a major role on the prediction of materials. Our demonstration of emergent computational approach that uses NN, ML and CM algorithms trained on DFT big and deep data illustrates a path for developing new materials with exclusive physical and chemical properties that would be difficult to achieve through experimental set up. To study the structural properties of polymer adsorbed on GE and SiO2, we have used the periodic density functional theory (DFT) technique that employs localized atomic orbital basis functions implication in SIESTA p ackages[32].

Results

Conclusion