Here to stay: Stability and structure of (6$\sqrt{3}\times$6$\sqrt{3}$)-R30$^\circ$ graphene on SiC(111) by all-electron DFT including van der Waals effects
Silicon carbide (SiC) is an excellent substrate for growth and manipulation of large scale, high ... more Silicon carbide (SiC) is an excellent substrate for growth and manipulation of large scale, high quality epitaxial graphene. On the carbon face (the ($\bar{1}\bar{1}\bar{1}$) or $(000\bar{1}$) face, depending on the polytype), the onset of graphene growth is intertwined with the formation of several competing surface phases, among them a (3$\times$3) precursor phase suspected to hinder the onset of controlled, near-equilibrium growth of graphene. Despite more than two decades of research, the precise atomic structure of this phase is still unclear. We present a new model of the (3$\times$3)-SiC-($\bar{1}\bar{1}\bar{1}$) reconstruction, derived from an {\it ab initio} random structure search based on density functional theory including van der Waals effects. The structure consists of a simple pattern of five Si adatoms in bridging and on-top positions on an underlying, C-terminated substrate layer, leaving one C atom per (3$\times$3) unit cell formally unsaturated. Simulated scanning...
We address the stability of the surface phases that occur on the C-side of 3C-SiC(1̄1̄1̄) at the ... more We address the stability of the surface phases that occur on the C-side of 3C-SiC(1̄1̄1̄) at the onset of graphene formation. In this growth range, experimental reports reveal a coexistence of several surface phases. By constructing an ab initio surface phase diagram using a van der Waals corrected density functional, we show that this coexistence can be explained by a Si-rich model for the unknown (3×3) reconstruction, the known (2×2)C adatom phase, and the graphene covered (2×2)C phase. The surface energies of these three phases cross at the chemical potential limit of graphite.
Graphene with its unique properties spurred the design of nanoscale electronic devices. Graphene ... more Graphene with its unique properties spurred the design of nanoscale electronic devices. Graphene films grown by Si sublimation on SiC surfaces are promising material combinations for future graphene applications based on existing semiconductor technologies. Obviously, the exact material properties of graphene depend on the its interaction with the substrate. Understanding the atomic and electronic structure of the SiC-graphene interface, is an important step to refine the growth quality. In this work, computational ab initio methods based on densityfunctional theory (DFT) are used to simulate the SiC-graphene interface on an atomistic level without empirical parameters. We apply state-of-the-art densityfunctional approximation (DFA), in particular the Heyd-Scuseria-Ernzerhof hybrid functional including van-der-Waals dispersion corrections to address the weak bonding between the substrate and graphene layers. DFA simulations allow to interpret and complement experimental results and ...
Japan Journal of Industrial and Applied Mathematics
We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers... more We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications-Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained.
The structure of the SiC(1 000) surface, the C-face of the {0001} SiC surfaces, is studied as a f... more The structure of the SiC(1 000) surface, the C-face of the {0001} SiC surfaces, is studied as a function of temperature and of pressure in a gaseous environment of disilane (Si 2 H 6). Various surface reconstructions are observed, both with and without the presence of an overlying graphene layer (which spontaneously forms at sufficiently high temperatures). Based on crosssectional scanning transmission electron microscopy measurements, the interface structure that forms in the presence of the graphene is found to contain 1.4-1.7 monolayers (ML) of Si, a somewhat counter-intuitive result since, when the graphene forms, the system is actually under C-rich conditions. Using ab initio thermodynamics, it is demonstrated that there exists a class of Si-rich surfaces containing about 1.3 ML of Si that are stable on the surface (even under C-rich conditions) at temperatures above ∼400 K. The structures that thus form consist of Si adatoms atop a Si adlayer on the C-face of SiC, with or without the presence of overlying graphene.
The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to De... more The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length- and time-scales that are unfeasible with first principles DFT. At the same time (and in contrast to empirical interatomic potentials and force-fields), DFTB still offers direct access to electronic properties such as the band-structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter-sets could be routinely adjusted for a given project. While fairly robust and transferable parameterization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper we propose a machine-learning (ML) approach to ...
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Papers by Lydia Nemec