Park, E.; Spiegel, M.: Effects of heat treatment on near surface elemental profiles of Fe–15Cr polycrystalline alloy. Corrosion Engineering, Science and Technology 40 (3), pp. 217 - 225 (2005)
Park, E.; Hüning, B.; Spiegel, M.: Annealing of Fe–15Cr alloy in N2–5%H2 gas mixture: Effect of hydrogen concentration. Defect and Diffusion Forum 237-240, p. 928 - 928 (2005)
Park, E.; Hüning, B.; Spiegel, M.: Evolution of near-surface concentration profiles of Cr during annealing of Fe–15Cr polycrystalline alloy. Applied Surface Science 249 (1-4), pp. 127 - 138 (2005)
Park, E.; Spiegel, M.: Development and Composition of the High Temperature Oxide Film Grown on Fe-15Cr during Annealing. Passivity 9, Paris, France, June 27, 2005 - July 01, 2005., (2005)
Park, E.; Hüning, B.; Spiegel, M.: Effects of heat treatment on the oxide layer of Fe–15 at.% Cr alloy surface. Proceedings of EUROCORR 04, Nice, France, 2004. Long Term Prediction and Modelling of Corrosion 1, (2004)
Park, E.; Spiegel, M.: Development and Composition of the High Temperature Oxide Film Grown on Fe-15Cr during Annealing. Passivity 9, Paris, France (2005)
Park, E.; Spiegel, M.: Oxidation resistance of alloys for flexible tubes in dry air and KCl containing atmospheres. Eurocorr 2005, Lisbon, Portugal (2005)
Park, E.; Hüning, B.; Borodin, S.; Rohwerder, M.; Spiegel, M.: Initial oxidation of Fe-Cr alloys: In situ STM amd ex-situ SEM studies. 6th International Conference on the Microscopy of Oxidation, Birmingham, UK (2005)
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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.