Kiener, D.; Motz, C.; Schöberl, T.; Jenko, M.; Dehm, G.: Determination of mechanical properties of copper at the micron scale. Advanced Engineering Materials 8 (11), pp. 1119 - 1125 (2006)
Riethmüller, J.; Dehm, G.; Affeldt, E. E.; Arzt, E.: Microstructure and mechanical behavior of Pt-modified NiAl diffusion coatings. International Journal of Materials Research 97 (6), pp. 689 - 698 (2006)
Wetscher, F.; Pippan, R.; Šturm, S.; Kauffmann, F.; Scheu, C.; Dehm, G.: TEM investigation of the structural evolution in a pearlitic steel deformed by high pressure torsion. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science 37 (6), pp. 1963 - 1968 (2006)
Kauffmann, F.; Ji, B.; Dehm, G.; Gao, H.; Arzt, E.: A quantitative study of the hardness in a superhard nanocrystalline titanium nitride/silicon nitride coating. Scripta Materialia 52 (12), pp. 1269 - 1274 (2005)
Dehm, G.; Edongué, H.; Wagner, T. A.; Oh, S. H.; Arzt, E.: Obtaining different orientation relationships for Cu films grown on (0001) α-Al2O3 substrates by magnetron sputtering. Zeitschrift für Metallkunde 96 (3), pp. 249 - 254 (2005)
Sauter, L. X.; Balk, T. J.; Dehm, G.; Nucci, J.; Arzt, E.: Hillock Formation and Thermal Stresses in Thin Au Films on Si Substrates. Materials Research Society Symposium Proceedings 875, O5.2, pp. 177 - 182 (2005)
Volkert, C. A.; Busch, S.; Heiland, B.; Dehm, G.: Transmission electron microscopy of fluorapatite-gelatine composite particles prepared using focused ion beam milling. Journal of Microscopy 214 (3), pp. 208 - 212 (2004)
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.