Heinz, W.; Pippan, R.; Dehm, G.: Investigation of the fatigue behavior of Al thin films with different microstructure. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 527 (29-30), pp. 7757 - 7763 (2010)
Fischer, F. D.; Cha, L.; Dehm, G.; Clemens, H. J.: Can local hot spots induce α2/γ lamellae during incomplete massive transformation of γ-TiAl alloys? Intermetallics 18 (5), pp. 972 - 976 (2010)
Fischer , F. D.; Waitz, T.; Scheu, C.; Cha, L.; Dehm, G.: Study of nanometer-scaled lamellar microstructure in a Ti–45Al–7.5Nb alloy – Experiments and modeling. Intermetallics 18 (4), pp. 509 - 517 (2010)
Matoy, K.; Detzel, T.; Müller , M.; Motz, C.; Dehm, G.: Interface fracture properties of thin films studied by using the micro-cantilever deflection technique. Surface and Coatings Technology 204 (6-7), pp. 878 - 881 (2009)
Dehm, G.: Miniaturized single-crystalline fcc metals deformed in tension: New insights in size-dependent plasticity. Progress in Materials Science 54 (6), pp. 664 - 688 (2009)
Oh, S. H.; Legros, M.; Kiener, D.; Dehm, G.: In situ observation of dislocation nucleation and escape in a submicrometre aluminium single crystal. Nature Materials 8 (2), pp. 95 - 100 (2009)
Kiener, D.; Motz, C.; Dehm, G.; Pippan, R.: Overview on established and novel FIB based miniaturized mechanical testing using in-situ SEM. International Journal of Materials Research 100 (8), pp. 1074 - 1087 (2009)
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.