Thermodynamic equilibrium conditions of graphene films on SiC

First-principles surface phase diagrams reveal that epitaxial monolayer graphene films on the Si side of 3C-SiC(111) can exist as thermodynamically stable phases in a narrow range of experimentally controllable conditions, defining a path to the highest-quality graphene films. Our calculations are based on a van der Waals corrected density functional. The full, experimentally observed (6 sqrt(3)x 6 sqrt(3))-R30 supercells for zero- to trilayer graphene are essential to describe the correct interface geometries and the relative stability of surface phases and possible defects

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

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

Graphene engineering

Die besonderen Eigenschaften von Graphen ermöglichen das Design von elektronischen Bauteilen im Nanometerbereich. Graphen kann auf der Oberfläche von Siliziumkarbonat (SiC) durch das Ausdampfen von Si epitaktisch gewachsen werden. Ein detailliertes Verständnis der atomaren und elektronischen Struktur der Grenzschicht zwischen Graphen und SiC ist ein wichtiger Schritt um die Wachstumsqualität zu verbessern. Wir nutzen Dichtefunktionaltheorie um das Hybridsystem Graphen-SiC auf atomarer Ebene zu beschreiben. Experimentelle Arbeiten auf der Si Seite von SiC haben gezeigt, dass die Grenzschicht (ZLG) durch eine teilweise kovalent gebundene Kohlenstofflage wächst; darüber bildet sich die erste Graphenlage (MLG). Durch das Konstruieren eines ab initio Oberflächenphasendiagrams zeigen wir, dass sowohl ZLG als auch MLG Gleichgewichtsphasen sind. Unsere Ergebnisse implizieren, dass Temperatur- und Druckbedingungen für den selbstbegrenzenden Graphenwachstum existieren. Wir zeigen, dass sich das Doping und die Riffellung von epitaktischem Graphene durch H-Interkalation reduzieren. Im Experiment unterscheidet sich das Graphenwachstum auf der C Seite qualitativ von der Si Seite. Zu Beginn des Graphenwachstums wird eine Mischung verschiedener Oberflächenphasen beobachtet. Wir diskutieren die Stabilität dieser konkurierenden Phasen. Die atomaren Strukturen von einigen dieser Phasen, inklusive der Graphen-SiC Grenzschicht, sind nicht bekannt wodurch die theoretische Beschreibung erschwert wird. Wir präsentieren ein neues Model für die bisher unbekannte (3x3) Rekonstruktion, das Si Twist Model. Die Oberflächenenergie vom Si Twist Model und von der bekannten (2x2)c Phase schneiden sich direkt an der Grenze zur Graphitbildung. Dies erklärt die experimentell beobachtete Phasenkoexistenz zu Beginn des Graphenwachstums. Wir schlussfolgern, dass auf der C Seite der kontrollierte Graphenewachstum durch Si-reiche Oberflächenphasen blockiert wird.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 graphene applications. Understanding the atomic and electronic structure of the SiC-graphene interface, is an important step to refine the growth quality. In this work, density-functional theory is used to simulate the SiC-graphene interface on an atomistic level without empirical parameters. Experimental work has shown that on the Si face of SiC, a partially covalently bonded carbon layer, the zero-layer graphene (ZLG), grows. On top of the ZLG layer forms mono-layer graphene (MLG) as large ordered areas and then few-layer graphene. By constructing an ab initio surface phase diagram, we show that ZLG and MLG are at least near equilibrium phases. Our results imply the existence of temperature and pressure conditions for self-limiting growth of MLG key to the large-scale graphene production. H intercalation significantly reduces both the corrugation and the graphene doping. Our calculations demonstrate that unsaturated Si atoms in the ZLG influence the electronic structure of graphene. The situation on the C face of SiC is very different. The experimental growth of large areas of graphene with well defined layer thickness is difficult. At the onset of graphene formation a phase mixture of different surface phases is observed. We will address the stability of the different occuring surface phases. However, the atomic structure of some of the competing surface phases, as well as of the SiC-graphene interface, is unknown. We present a new model for the (3x3) reconstruction, the Si twist model. The surface energies of this Si twist model, the known (2x2)c adatom phase, and a graphene covered (2x2)c phase cross at the chemical potential limit of graphite, which explains the observed phase mixture. We argue that well-controlled graphene formation is hindered by Si-rich surface phases

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

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 fitting Vrep, using Gaussian Process Regression (GPR). The use of GPR circumvents the need for non-linear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen and oxygen. Overall, the new approach removes focus from the choice of functional form and parameterization procedure, in favour of a data-driven philosophy

Formation of graphene atop a Si adlayer on the C-face of SiC

The structure of the SiC(000 (1) over bar) 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 (Si2H6). 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 cross-sectional 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 similar to 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.Peer reviewe

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent hievements of the ELPA-AEO (Eigen-value 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