Ostwald, C.; Grabke, H. J.: Initial Oxidation and Chromium Diffusion. I. Effects of Surface Working on 9-20% Cr Steels. Corrosion Science 46 (5), pp. 1113 - 1127 (2004)
Grabke, H. J.; Spiegel, M.; Zahs, A.: Role of Alloying Elements and Carbides in the Chlorine-induced Corrosion of Steels and Alloys. Materials Research 7 (1), pp. 89 - 95 (2004)
Grabke, H.-J.; Tôkei, Z. S.; Ostwald, C.: Initial Oxidation of a 9 % CrMo- and a 12 % CrMoV – Steel. Steel Research International 75 (1), pp. 38 - 46 (2004)
Grabke, H. J.; Müller-Lorenz, E. M.; Zinke, M.: Metal Dusting Behaviour of Welded Ni-Base Alloys with Different Surface Finish. Material and Corrosion 54, pp. 785 - 792 (2003)
Pippel, E.; Woltersdorf, J.; Grabke, H. J.: Microprocesses of Metal Dusting on Iron - Nickel Alloys and their Dependence on Composition. Material and Corrosion 54 (10), pp. 747 - 751 (2003)
Spiegel, M.; Zahs, A.; Grabke, H. J.: Fundamental aspects of chlorine induced corrosion in power plants. Materials at High Temperatures 20, 2, pp. 153 - 159 (2003)
Moszynski, D.; Grabke, H. J.; Schneider, A.: Effect of sulphur on the formation of graphite at the surface of carburized iron. Surface and Interface Analysis 34, pp. 380 - 383 (2002)
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.
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…
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.
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…