Lange, B.: Limitierungen der p-Dotierbarkeit von Galliumnitrid: Eine Defektstudie von GaN:Mg auf Basis der Dichtefunktionaltheorie. Dissertation, Universität Paderborn, Paderborn, Germany (2012)
Hamou, F. R.: Numerical Investigation of Scanning Electrochemical Potential Microscopy (SECPM). Dissertation, Fakultät für Physik und Astronomie der Ruhr-Universität, Bochum, Germany (2010)
Abu-Farsakh, H.: Understanding the interplay between thermodynamics and surface kinetics in the growth of dilute nitride alloys from first principles. Dissertation, University of Paderborn, Paderborn, Germany (2010)
Marquardt, O.: Implementation and application of continuum elasticity theory and a k.p-model to investigate optoelectronic properties of semiconductor nanostructures. Dissertation, University of Paderborn, Paderborn, Germany (2010)
Grabowski, B.: Towards ab initio assisted materials design: DFT based thermodynamics up to the melting point. Dissertation, University of Paderborn, Paderborn, Germany (2009)
Torres, E.: DFT Study of Alkanethiol Self-assembled Monolayers on Gold(111) Surfaces. Dissertation, Ruhr-Universität-Bochum, Fakultät für Physik und Astronomie, Bochum, Germany (2009)
Dick, A.: Ab initio STM and STS simulations on magnetic and nonmagnetic metallic surfaces. Dissertation, University of Paderborn, Paderborn, Germany (2008)
Surendralal, S.; Todorova, M.: Automated Calculations for Charged Point Defects in Magnesium Oxide and Iron Oxides. Master, Ruhr-Universität Bochum, GermanyRuhr-Universität Bochum, Bochum, Germany (2016)
Sözen, H. I.: Ab initio investigations on the energetics and kinetics of defects in Fe–Al alloys. Master, Ruhr-Universität Bochum, Bochum, Germany (2014)
Tillack, N.: Chemical Trends in the Yttrium-Oxide Precipitates in Oxide Dispersion Strengthened Steels: A First-Principles Investigation. Master, Ruhr-Universität Bochum, Bochum, Germany (2012)
Kim, O.: Ab-initio study of formation and interaction energies in steel and their relations to the solubility limit of carbon in austenite and ferrite. Master, RWTH-Aachen, Aachen, Germany (2007)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…