Brink, T.; Kim, D.; Dehm, G.: Atomistic computer simulations of the influence of grain boundary phases on segregation. DPG-Frühjahrstagung 2025, Regensburg, Germany (2025)
Bhat, M. K.; Brink, T.; Ding, H.; Jung, C.; Best, J. P.; Dehm, G.: Influence of the Structure and Chemistry of Σ5 Grain Boundaries on Microscale Strengthening in Cu Bicrystals. TMS Annual Meeting and Exhibition 2024, Orlando, FL, USA (2024)
Brink, T.; Langenohl, L.; Ahmad, S.; Liebscher, C.; Dehm, G.: Atomistic Modeling of the Thermodynamics of Grain Boundaries in fcc Metals. 19th International Conference on Diffusion in Solids and Liquids, Crete, Greece (2023)
Brink, T.; Bhat, M. K.; Best, J. P.; Dehm, G.: Grain-boundary segregation effects on bicrystal Cu pillar compression. DPG Spring Meeting, Dresden, Germany (2023)
Brink, T.: Atomistic simulations of grain boundary phases: From thermodynamics to mechanics. Seminar Materials Science and Technology at the Ruhr-Universität Bochum
, Bochum, Germany (2023)
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)
Brink, T.: Thermodynamics of grain boundary phases in fcc metals: Using atomistic simulations to augment and extend experimental insights. Materials Science Colloquium at the Technische Universität Darmstadt, Darmstadt, Germany (2022)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…