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)
Alkauskas, A.; Deak, P.; Neugebauer, J.; Pasquarello, A.; van de Walle, C. G. (Eds.): Advanced Calculations for Defects in Solids - Electronic Structure Methods - Preface (Special issue). Physica Status Solidi B 248, (1) (2011), 17-18 pp.
Poul, M.; Huber, L.; Bitzek, E.; Neugebauer, J.: Systematic Structure Datasets for Machine Learning Potentials: Application to Moment Tensor Potentials of Magnesium and its Defects. arXiv (2022)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.