Graphene quantum dot states near defects

Schattauer, Christoph; Linhart, Lukas; Fabian, Thomas; Jawecki, Tobias; Auzinger, Winfried; Libisch, Florian

doi:10.1103/physrevb.102.155430

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…6 The results are relevant to other confined systems formed in two-dimensional materials by external fields, [16][17][18][19][20][21][22][23][24][25] as well as to general hybrid graphene-based systems including defects and heterostructures. [26][27][28][29][30][31][32][33] Section II presents the physical model of the graphene quantum dot and explains with some qualitative arguments how the discrete levels of the dot emerge from the bulk Lan-dau levels. The energy range of interest is also specified in Sec.…”

Section: Introductionmentioning

confidence: 99%

Energy spectra of graphene quantum dots induced between Landau levels

Giavaras

2021

Preprint

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When an energy gap is induced in monolayer graphene the valley degeneracy is broken and the energy spectrum of a confined system such as a quantum dot, becomes rather complex exhibiting many irregular level crossings and small energy spacings which are very sensitive to the applied magnetic field. Here we study the energy spectrum of a graphene quantum dot that is formed between Landau levels, and show that for the appropriate potential well the dot energy spectrum in the first Landau gap can have a simple pattern with energies coming from one of the two valleys only. This part of the spectrum has no crossings, has specific angular momentum numbers, and the energy spacing can be large enough, consequently, it can be probed with standard spectroscopic techniques. The magnetic field dependence of the dot levels as well as the effect of the massinduced energy gap are examined, and some regimes leading to a controllable quantum dot are specified. At high magnetic fields and negative angular momentum a simple approximate method to the Dirac equation is developed which gives further insight into the physics. The approximate energies exhibit the correct trends and agree well with the exact energies.

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Section: Introductionmentioning

confidence: 99%

Energy spectra of graphene quantum dots induced between Landau levels

Giavaras

2021

Preprint

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Electronic and magnetic properties of stacked graphene quantum dots

Tiutiunnyk

Laroze

Correa

2023

Diamond and Related Materials

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Machine learning sparse tight-binding parameters for defects

et al. 2022

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We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them for our further investigations. We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure, local density of states, transport and level spacing simulations for two common defects in single layer graphene. Our machine learning approach achieves results comparable to maximally localized Wannier functions (i.e., DFT accuracy) without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time. It is general and can be applied to a wide range of other materials, enabling accurate large-scale simulations of material properties in the presence of different defects.

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Graphene quantum dot states near defects

Cited by 5 publications

References 46 publications

Energy spectra of graphene quantum dots induced between Landau levels

Energy spectra of graphene quantum dots induced between Landau levels

Electronic and magnetic properties of stacked graphene quantum dots

Machine learning sparse tight-binding parameters for defects

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