New Physics of the 30∘30^\circ Partial Dislocation in Silicon Revealed through {\em Ab Initio} Calculation

Based on {\em ab initio} calculation, we propose a new structure for the fundamental excitation of the reconstructed 30$^\circ$ partial dislocation in silicon. This soliton has a rare structure involving a five-fold coordinated atom near the dislocation core. The unique electronic structure of this defect is consistent with the electron spin resonance signature of the hitherto enigmatic thermally stable R center of plastically deformed silicon. We present the first {\em ab initio} determination of the free energy of the soliton, which is also in agreement with the experimental observation. This identification suggests the possibility of an experimental determination of the density of solitons, a key defect in understanding the plastic flow of the material.Comment: 6 pages, 5 postscript figure

Wenn die Akzeptanz der Supportangebote sinkt – Fehlentwicklung oder strukturelle Notwendigkeit

Das Supportangebot des E-Learning Zentrums der TU Wien wurde im Rahmen des Projekts Delta 3 entwickelt und war damit an der Nominierung als Finalist des Medida Prix 2007 nominiert. In den zwölf Monaten seit der Einreichung ging die Nachfrage – speziell für die Weiterbildungsworkshops – deutlich zurück. Die möglichen Gründe dafür werden selbstkritisch und auch aus strategischer Sicht analysiert, um daraus potenzielle Verbesserungsmaßnahmen ableiten zu können. (DIPF/ Orig.

Machine learning based interatomic potential for amorphous carbon

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range potential can achieve in carbon structures; then, using a hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with a state-of-the-art empirical potential. Exemplary applications of the GAP model to surfaces of “diamondlike” tetrahedral amorphous carbon ($\textit{ta}$-C) are presented, including an estimate of the amorphous material’s surface energy and simulations of high-temperature surface reconstructions (“graphitization”). The presented interatomic potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures.V.L.D. gratefully acknowledges a postdoctoral fellowship from the Alexander von Humboldt Foundation and support from the Isaac Newton Trust (Trinity College Cambridge). This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) via EPSRC Grant No. EP/K014560/1

A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an out- standing challenge. Here we show that a universally applicable force field for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum- mechanical results. Our model is fitted to density-functional theory plus many-body dis- persion (DFT+MBD) data; its accuracy is demonstrated for the exfoliation of black and violet phosphorus (yielding monolayers of “phosphorene” and “hittorfene”); its transfer- ability is shown for the transition between the molecular and network liquid phases. An application to a phosphorene nanoribbon on an experimentally relevant length scale ex- emplifies the power of accurate and flexible ML-driven force fields for next-generation materials modelling. The methodology promises new insights into phosphorus as well as other structurally complex, e.g., layered solids that are relevant in diverse areas of chem- istry, physics, and materials science

Deadlocks and waiting times in traffic jam

In a city of right moving and upmoving cars with hardcore constraint, traffic jam occurs in the form of bands. We show how the bands are destroyed by a small number of strictly left moving cars yielding a deadlock phase with a rough edge of left cars. We also show that the probability of waiting time at a signal for a particular tagged car has a power law dependence on time, indicating the absence of any characteristic time scale for an emergent traffic jam. The exponent is same for both the band and the deadlock cases. The significances of these results are discussed.Comment: 8 pages including 4 eps figures, one in colour, uses revtex to appear in Physica

Tailoring Graphene with Metals on Top

We study the effects of metallic doping on the electronic properties of graphene using density functional theory in the local density approximation in the presence of a local charging energy (LDA+U). The electronic properties are sensitive to whether graphene is doped with alkali or transition metals. We estimate the the charge transfer from a single layer of Potassium on top of graphene in terms of the local charging energy of the graphene sheet. The coating of graphene with a non-magnetic layer of Palladium, on the other hand, can lead to a magnetic instability in coated graphene due to the hybridization between the transition-metal and the carbon orbitals.Comment: 5 pages, 4 figure

Blackbox Lernprozess und informelle Lernszenarien

Im Kontrast zu weit verbreiteten Auffassungen ist es aus der Sicht von Lernpsychologie und Hirnforschung nicht möglich, individuelle Lernprozesse exakt zu steuern. Im Gegenteil: Der individuelle Lernprozess stellt sich als Blackbox dar, deren Output immer wieder nur erstaunt zur Kenntnis genommen werden kann. Alle Versuche, dieses Problem zu lösen, erweisen sich regelmäßig als Ressourcenverschwendung. Als deutlich effizienter könnte es sich hingegen offenbaren, informelle Lernformen als Methode der Wahl massiv einzusetzen und somit den - ohnehin unrealistischen - Kontrollanspruch als Lehrende endgültig aufzugeben. (DIPF/Orig.

Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science

The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems from amorphous carbon, universal materials modelling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimisation to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient, and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies