Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion. Physical Review Materials 8 (6), 063602 (2024)
Chauniyal, A.; Dehm, G.; Janisch, R.: On the role of pre-existing defects in influencing hardness in nanoscale indentations — Insights from atomistic simulations. Journal of the Mechanics and Physics of Solids 154, 104511 (2021)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Deformation mechanism of complexions in a Cu grain boundary under shear. FEMS EUROMAT 2023, Frankfurt am Main, Germany (2023)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Disconnection activation in complexions of a Cu grain boundary under shear. 19th International Conference on Diffusion in Solids and Liquids (DSL-2023), Heraklion, Greece (2023)
Pemma, S.; Brink, T.; Janisch, R.; Dehm, G.: Stress driven grain boundary migration for different complexions of a Cu tilt grain boundary. Materials Science and Engineering Congress 2022, Darmstadt, Germany (2022)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Atomistic simulation study of grain boundary migration for different complexions in copper. DPG-Tagung, Virtual (2021)
Arigela, V. G.; Kirchlechner, C.; Janisch, R.; Hartmaier, A.; Dehm, G.: Setup of a microscale fracture apparatus to study the interface behaviour in materials at high temperatures. Materials Day 2016, Ruhr Universitat Bochum, Bochum, Germany (2016)
Wang, Z.: Investigation of crystallographic character and molten-salt-corrosion properties of grain boundaries in a stainless steel using EBSD and ab-initio calculations. Dissertation, Ruhr-Universität Bochum, Bochum, Germany (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.