Brinckmann, S.: Using Simulations to Investigate the Apparent Fracture Toughness of Microcantilevers. STKS-ICAMS-Seminar, RUB Bochum, Bochum, Germany (2018)
Brinckmann, S.: Understanding the fracture toughness for brittle and ductile materials at the microscale. Materials Science and Engineering-MSE 2018, Darmstadt, Germany (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)
Brinckmann, S.: Microscale Materials Tribology: Severe Deformation of Pearlite. Talk at Institut für Konstruktionswissenschaften und Technische Logistik, Technische Universität Wien, Wien, Austria (2017)
Brinckmann, S.: Severe Deformation of Pearlite during Microscale Tribology. Talk at Erich Schmid Institute für Materialwissenschaft, Leoben, Austria (2017)
Brinckmann, S.; Kirchlechner, C.; Dehm, G.; Matoy, K.: Using simulations to investigate the apparent fracture toughness of microcantilevers. Nanomechanical Testing in Materials Research and Development VI, Dubrovnik, Croatia (2017)
Duarte, M. J.; Fang, X.; Brinckmann, S.; Dehm, G.: In-situ nanoindentation of hydrogen bcc Fe–Cr charged surfaces: Current status and future perspectives. Frontiters in Material Science & Engineering workshop: Hydrogen Interaction in Metals, Max-Planck Institut für Eisenforschung, Düsseldorf, Germany (2017)
Brinckmann, S.; Fink, C.; Dehm, G.: Severe Microscale Deformation of Pearlite and Cementite. 2017 MRS Spring Meeting & Exhibits, Phoenix, AZ, USA (2017)
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.