Arigela, V. G.; Oellers, T.; Ludwig, A.; Kirchlechner, C.; Dehm, G.: Mechanical characterization of copper thin films produced by photolithography with a novel microscale high temperature loading rig. The International Conference on Experimental Mechanics, (ICEM) 2018, Brussels, Belgium (2018)
Lee, S.; Liebscher, C.; Dehm, G.: In-situ TEM study on deformation behaviors of CrMnFeCoNi single crystal high entropy alloys. European Solid Mechanics Conference (ESMC) , Bologna, Italy (2018)
Li, J.; Dehm, G.; Kirchlechner, C.: Dislocation source activation by nanoindentation in single crystals and at grain boundaries. E-MRS Spring, Strasbourg, France (2018)
Duarte, M. J.; Fang, X.; Brinckmann, S.; Dehm, G.: New approaches for in-situ nanoindentation of hydrogen charged alloys: insights on bcc FeCr alloys. DPG Spring Meeting of the Condensed Matter Section, Berlin, Germany (2018)
Dehm, G.: “Mechanical microscopy”: Resolving the mechanical behavior and underlying mechanisms of materials with high spatial resolution. The 18th Israel Materials Engineering Conference (IMEC-18), Dead Sea, Israel (2018)
Li, J.; Dehm, G.; Kirchlechner, C.: Differences in dislocation source activation stress in the grain interior and at twin boundaries using nanoindentation. Nanobruecken 2018, Erlangen, Germany (2018)
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…