Raabe, D.: The Interplay of Lattice Defects and Chemistry at Atomic Scale and Why it Matters for the Properties of Materials. Van Horn Distinguished Lecturer Series, Cleveland, OH, USA (2023)
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)
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…