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Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks

Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks


Title: Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks
Author: Nemnes, G. A.
Mitran, T. L.
Manolescu, Andrei   orcid.org/0000-0002-0713-4664
Date: 2019-05-16
Language: English
Scope: 1-8
University/Institute: Háskólinn í Reykjavík
Reykjavik University
School: Tækni- og verkfræðideild (HR)
School of Science and Engineering (RU)
ISSN: 1687-4110
1687-4129 (eISSN)
DOI: 10.1155/2019/6960787
Subject: General Materials Science; Machine learning; Artificial intelligence; Artificial neural networks; Nanotechnology; Nanomaterials; Graphene; Boron; Nitride; Efnisfræði; Nanótækni; Vélrænt nám; Gervigreind; Hermilíkön; Taugakerfi; Ástand efnis; Bór; Nítröt
URI: https://hdl.handle.net/20.500.11815/1883

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Citation:

Nemnes, G. A., Mitran, T. L., & Manolescu, A. (2019). Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks. Journal of Nanomaterials, 6960787. https://doi.org/10.1155/2019/6960787

Abstract:

The electronic properties of graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined ab initio density functional theory calculations and machine-learning techniques. The energy gaps of the quasi-0D graphene-based systems, defined as the differences between LUMO and HOMO energies, depend not only on the sizes of the hBN domains relative to the size of the pristine graphene nanoflake but also on the position of the hBN domain. The range of the energy gaps for different configurations increases as the hBN domains get larger. We develop two artificial neural network (ANN) models able to reproduce the gap energies with high accuracies and investigate the tunability of the energy gap, by considering a set of GNFs with embedded rectangular hBN domains. In one ANN model, the input is in one-to-one correspondence with the atoms in the GNF, while in the second model the inputs account for basic structures in the GNF, allowing potential use in upscaled systems. We perform a statistical analysis over different configurations of ANNs to optimize the network structure. The trained ANNs provide a correlation between the atomic system configuration and the magnitude of the energy gaps, which may be regarded as an efficient tool for optimizing the design of nanostructured graphene-based materials for specific electronic properties.

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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cite

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