Marx, V. M.; Kirchlechner, C.; Zizak, I.; Dehm, G.; Cordill, M. J.: In-situ fracture study of thin Cu films on polyimide substrate. GDRi MECANO General Meeting 2012, Ecole de Mines, Paris, France (2012)
Eiper, E.; Martinschitz, K. J.; Dehm, G.; Kečkéš, J.: Size effect in metallic thin films characterized by low-temperature X-ray diffraction. Gordon Research Conference on thin film & smallscale mechanical behavior , Colby College Waterville, Maine, USA (2006)
Rester, M.; Kiener, D.; Kreuzer, H. G.M.; Dehm, G.; Motz, C.: Microstructural investigation of the deformation zone below nanoindents in copper, silver and nickel. Hysitron Workshop and Usermeeting, München, Germany (2006)
Dehm, G.: Mechanische Eigenschaften in kleinen Dimensionen. Lecture: Vorlesung (3LP), SS 2015, Ruhr-Universität Bochum, Bochum, Germany, May 18, 2015 - May 22, 2015
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
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.
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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…