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Erschienen in:
Making Computer Materials Real: The Predictive Power of First-Principles Molecular Dynamics Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

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

verfasst von : Tudor Luca Mitran, George Alexandru Nemnes

Erschienen in: Theory and Simulation in Physics for Materials Applications

Verlag: Springer International Publishing

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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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Metadaten
Titel
Prediction of Energy Gaps in Graphene—Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks
verfasst von
Tudor Luca Mitran
George Alexandru Nemnes
Copyright-Jahr
2020
Buch
Theory and Simulation in Physics for Materials Applications

Print ISBN: 978-3-030-37789-2

Electronic ISBN: 978-3-030-37790-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-37790-8_11

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