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)
Yang, B.; Motz, C.; Grosinger, W.; Kammrath, W.; Dehm, G.: Tensile behaviour of micro-sized copper wires studied by a novel fibre tensile module. International Journal of Materials Research 99 (7), pp. 716 - 724 (2008)
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
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.
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…