Inkson, B. J.; Dehm, G.; Peng, Y.: Dynamical growth of Cu-Pt nanowires with a nanonecklace morphology. Nanotechnology 18 (41), 415601, pp. 1 - 5 (2007)
Oh, S. H.; Legros, M.; Kiener, D.; Gruber, P. A.; Dehm, G.: In situ TEM straining of single crystal Au films on polyimide: Change of deformation mechanisms at the nanoscale. Acta Materialia 55 (16), pp. 5558 - 5571 (2007)
Kiener, D.; Motz, C.; Rester, M.; Jenko, M.; Dehm, G.: FIB damage of Cu and possible consequences for miniaturized mechanical tests. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 459 (1-2), pp. 262 - 272 (2007)
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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.
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.