Elkot, M.; Sun, B.; Ponge, D.; Raabe, D.: Tackling hydrogen embrittlement sensitivity and poor low-temperature toughness of austenitic high manganese lightweight steel. Thermec 2023 - International Conference on PROCESSING & MANUFACTURING OF ADVANCED MATERIALS, Vienna, Austria (2023)
Elkot, M.; Sun, B.; Ponge, D.; Raabe, D.: The deceit of steel strength ductility diagrams: A case study on high manganese lightweight steel. 7th International Conference of Engineering Against Failure ICEAF 2023, Spetses, Greece (2023)
Elkot, M.; Sun, B.; Zhou, X.; Ponge, D.; Raabe, D.: Grain boundary κ-carbides in high manganese lightweight steel: degradation assessment and potential solutions. 5th International High Manganese Steel Conference 2022, online, Linz, Austria (2022)
Liu, C.; Roters, F.; Raabe, D.: Finite strain crystal plasticity-phase field modeling of deformation twinning and dislocation slip interaction in hexagonal materials. 18th European Mechanics of Materials Conference, online, Oxford, UK (2022)
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…