Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites
| dc.citation.articleNumber | 15111 | en_US |
| dc.citation.journalTitle | Scientific Reports | en_US |
| dc.citation.volumeNumber | 11 | en_US |
| dc.contributor.author | Shayeganfar, Farzaneh | en_US |
| dc.contributor.author | Shahsavari, Rouzbeh | en_US |
| dc.date.accessioned | 2021-08-20T20:24:34Z | en_US |
| dc.date.available | 2021-08-20T20:24:34Z | en_US |
| dc.date.issued | 2021 | en_US |
| dc.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. | en_US |
| dc.identifier.citation | Shayeganfar, Farzaneh and Shahsavari, Rouzbeh. "Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites." <i>Scientific Reports,</i> 11, (2021) Springer Nature: https://doi.org/10.1038/s41598-021-94085-9. | en_US |
| dc.identifier.digital | s41598-021-94085-9 | en_US |
| dc.identifier.doi | https://doi.org/10.1038/s41598-021-94085-9 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/111316 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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