Fig. 8: Quantum dot benchmark of vacancy defect.
From: Machine learning sparse tight-binding parameters for defects
Level spectrum landscapes calculated with different TB parametrizations of the double vacancy in SLG compared against the Wannier parametrization [a MLP(10NN), b Slater–Koster, c MLP(5NN), d MLP(3NN)]. Inset shows schematic sketch of the underlying system: we calculate the level spectrum (orbital and valley quantum number, spin is omitted) as a function of the position of an STM-tip (brown) induced (smoothly confined) GQD relative to an embedded defect in a large graphene flake (gray rectangle). Dotted gray lines represent the level structure of a pristine GQD with doubly degenerate orbitals.
