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

  • Tudor Luca Mitran
  • George Alexandru NemnesEmail author
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Part of the Springer Series in Materials Science book series (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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tudor Luca Mitran
    • 1
  • George Alexandru Nemnes
    • 1
    • 2
    • 3
    Email author
  1. 1.Horia Hulubei National Institute for R&D in Physics and Nuclear EngineeringBucharest, MagureleRomania
  2. 2.Faculty of PhysicsUniversity of BucharestMagureleRomania
  3. 3.Research Institute of the University of Bucharest (ICUB)BucharestRomania

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