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)
Kiener, D.; Grosinger, W.; Dehm, G.; Pippan, R.: A further step towards an understanding of size-dependent crystal plasticity: In situ tenison experiments of miniaturized single-crystal copper samples. Acta Materialia 56 (3), pp. 580 - 592 (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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.