Tsybenko, H.; Xia, W.; Dehm, G.; Brinckmann, S.: On the commensuration of plastic plowing at the microscale. Nanobrücken 2020: Nanomechanical Testing Conference & Bruker User Meeting, Düsseldorf, Germany (2020)
Duarte, M. J.; Fang, X.; Brinckmann, S.; Dehm, G.: Hydrogen-microstructure interactions in bcc FeCr alloys by in-situ nanoindentation. ECI, Nanomechanical Testing in Materials Research and Development VI, Dubrovnik, Croatia (2017)
Fink, C.; Brinckmann, S.; Dehm, G.: Nanotribology and Microstructure Evolution in Pearlite. 3rd European Symposium on Friction, Wear and Wear Protection, Karlsruhe, Germany (2014)
Brinckmann, S.: Dislocation emission from short penny-shaped cracks: A multiscale model of atomistic & dislocation dynamics. Nanomechanical Testing in Materials Research and Development IV, Olhão (Algarve), Portugal (2013)
Patil, P.: Influence of plastic anisotropy on the deformation behaviour of Austenitic stainless-steel during single micro-asperity wear. Dissertation, Ruhr-Uiversität-Bochum (2023)
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
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…
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.