Characterization of crystal defects during molecular dynamics simulations of mechanical deformation

Für die Untersuchung des Verformungsverhaltens von Festkörpern sind atomistische Simulationen ein geeignetes Verfahren. Durch die steigende verfügbare Rechenleistung, können größere Volumen simuliert werden. Die damit einhergehende Steigerung der generierten Rohdaten bedarf einer geeigneter Methoden, um die zu untersuchenden Materialeigenschaften abzuleiten. In dieser Arbeit werden Analysemethoden für atomistische Simulationen entwickelt. Versetzungen, als Träger der plastischen Verformung, werden aus den Rohdaten extrahiert und charakterisiert. In Formgedächtnislegierungen werden zusätzlich Phasengrenzen bestimmt. Die Methoden werden in Nanoindentierungssimulationen von Kupfer zur Bestimmung der geometrisch notwendigen Versetzungsdichte und der Versetzungsevolution verwendet. In Formgedächtnislegierungen wird die Interaktion von Versetzungen und des austenitisch-martensitischen Phasenübergangs untersucht.Atomistic simulations are a suitable method to study the deformation behavior of crystalline solids. Due to the increasing computational power, simulations of larger volumes are becoming feasible. In order to extract the material properties to be investigated from the increasing amount of raw data, suitable methods are required. In this thesis, new analysis methods are presented. Dislocations, being the carrier of plastic deformation, are directly extracted and characterized from atomistic raw data. In shape memory alloys additionally phase boundaries are identified. These methods are applied in nanoindentation simulations in copper to identify the density of geometrically necessary dislocation and the evolution of dislocations. In shape memory alloys the interaction between dislocations and the austenitic-martensitic phase transformation is studied

Atomistic Simulations of Compression Tests on Ni_3Al Nanocubes

International audienceMater. Res. Lett., 2014. doi: 10.1080/21663831.2013.87888

Semantic high-level features for automated cross-modal slideshow generation

This paper describes a technical solution for automated slideshow generation by extracting a set of high-level features from music, such as beat grid, mood and genre and intelligently combining this set with image high-level features, such as mood, daytime- and scene classification. An advantage of this high-level concept is to enable the user to incorporate his preferences regarding the semantic aspects of music and images. For example, the user might request the system to automatically create a slideshow, which plays soft music and shows pictures with sunsets from the last 10 years of his own photo collection.The high-level feature extraction on both, the audio and the visual information is based on the same underlying machine learning core, which processes different audio- and visual-low- and mid-level features. This paper describes the technical realization and evaluation of the algorithms with suitable test databases

Atomistic Simulations of Compression Tests on Ni₃Al Nanocubes

<div><p>The deformation behaviour of nano-sized Ni₃Al cubes with {100} side surfaces is investigated under uniaxial compression using constant-temperature molecular dynamics simulations at 300 K. The simulations reproduce key features of recently performed nanocompression experiments, namely the lack of strain hardening, homogeneous deformation of the entire sample and overall high stress levels of the order of 3–5 GPa. According to the simulations, the critical initial step is the formation of a pseudo-twin structure, which then further deforms by Shockley partial dislocations. These deformation mechanisms differ significantly from bulk Ni₃Al and are rationalized in terms of generalized stacking fault energies and resolved shear stresses.</p></div