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Prediction of Energy Gaps in Graphene—Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks

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Theory and Simulation in Physics for Materials Applications

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 296))

Abstract

Machine learning methods are currently applied in conjunction with ab initio density functional theory (DFT) simulations in order to establish computationally efficient alternatives for high-throughput processing in atomistic computations. The proposed method, based on artificial neural networks (ANNs), was used to predict the HOMO-LUMO energy gap in quasi-0D graphene nanoflake systems with randomly generated boron nitride embedded regions. Several artificial neural network (ANN) algorithms were tested in order to optimize the network parameters for the problem at hand. The trained ANNs prove to be computationally efficient at determining the energy gap with good accuracy and show a significant speedup over the classical DFT approach.

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Acknowledgements

This work was supported by the Romanian Ministry of Research and Innovation under the project PN19060205/2019 and by the Romania-JINR cooperation project.

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Authors and Affiliations

  1. Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering, Str. Reactorului no. 30, P.O. BOX MG-6, Bucharest, Magurele, Romania

    Tudor Luca Mitran & George Alexandru Nemnes

  2. Faculty of Physics, University of Bucharest, Str. Atomistilor, nr. 405, Magurele, Romania

    George Alexandru Nemnes

  3. Research Institute of the University of Bucharest (ICUB), 36-46 Mihail Kogalniceanu Blvd, 050107, Bucharest, Romania

    George Alexandru Nemnes

Authors
  1. Tudor Luca Mitran
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Correspondence to George Alexandru Nemnes .

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Editors and Affiliations

  1. CARMA, School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, NSW, Australia

    Elena V. Levchenko

  2. SPEC, CEA, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France

    Yannick J. Dappe

  3. IPCMS, CNRS, University of Strasbourg, Strasbourg, France

    Guido Ori

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Mitran, T.L., Nemnes, G.A. (2020). Prediction of Energy Gaps in Graphene—Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks. In: Levchenko, E., Dappe, Y., Ori, G. (eds) Theory and Simulation in Physics for Materials Applications. Springer Series in Materials Science, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-37790-8_11

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