Dehm, G.; Liebscher, C.: In situ TEM study of deformation and phase transformation mechanisms in chemically complex alloys. Symposium In-situ & Environmental Microscopy, 20th International Microscopy Congress, Busan, Korea (2023)
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.; Frommeyer, L.; Freitas, R.; Frolov, T.; Pemma, S.; Liebscher, C.; Dehm, G.: Diffusionless congruent grain boundary phase transitions in metals: Simulation and experimental imaging. 2021 Fall Meeting of the European Materials Research
Society
, Virtual (2021)
Frommeyer, L.; Brink, T.; Freitas, R.; Frolov, T.; Dehm, G.; Liebscher, C.: Congruent grain boundary phase transformations revealed by STEM in pure copper. Microscopy conference Joint Meeting of Dreiländertagungn & Multinational Congress on Microscopy MC 2021, virtual, Vienna, Austria (2021)
Liebscher, C.: How do grain boundaries transform in metallic alloys? Institute of Material Physics, Westfälische Wilhelms-Universität Münster, Online Colloqium, Münster, Germany (2021)
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
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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…