2020
DOI: 10.1007/978-3-030-37790-8_11
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Prediction of Energy Gaps in Graphene—Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks

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Cited by 3 publications
(3 citation statements)
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“…This set-up of the feature vectors takes into account structural information, as well as chemical neighborhood data, which enables an efficient and an accurate prediction of the targeted quantities. A related approach was successfully employed for the investigation of energy gaps in hybrid graphene-hexagonal boron nitride nanoflakes [39,40].…”
Section: Models and Methodsmentioning
confidence: 99%
“…This set-up of the feature vectors takes into account structural information, as well as chemical neighborhood data, which enables an efficient and an accurate prediction of the targeted quantities. A related approach was successfully employed for the investigation of energy gaps in hybrid graphene-hexagonal boron nitride nanoflakes [39,40].…”
Section: Models and Methodsmentioning
confidence: 99%
“…In defining the features we introduce one parameter, the cutting radius R c , which defines local groups of atoms. These groups can be single atoms, quadruplets as defined in [30,31], but also larger groups of atoms. The first step is to form a database of distinct features.…”
Section: Feature Selectionmentioning
confidence: 99%
“…The energy gaps are susceptive to the ratio between the BN and graphene domains, but also to the patterns formed by constituent atoms [29]. Previous studies have shown that feature vectors constructed based on chemical neighborhood are able to reproduce the energy gaps [30,31]. However, there was no indepth analysis concerning the most advantageous selection of features.…”
Section: Introductionmentioning
confidence: 99%