Friák, M.; Sander, B.; Ma, D.; Raabe, D.; Neugebauer, J.: Theory-guided design of Ti-binaries for human implants. XVI. International Materials Research Congress, Cancun (Merrida), Mexico (2007)
Friák, M.; Sander, B.; Ma, D.; Raabe, D.; Neugebauer, J.: Ab initio prediction of elastic and thermodynamic properties of metals. Seminar in Friedrich-Alexander-Universitaet, Erlangen-Nürnberg, Germany (2007)
Friak, M.; Sander, B.; Ma, D.; Raabe, D.; Neugebauer, J.: Theory-guided design of Ti–Nb alloys for biomedical applications. 1st International Conference on Material Modelling, Dortmund, Germany (2009)
Friák, M.; Ma, D.; Sander, B.; Raabe, D.; Neugebauer, J.: Bottom up design of novel titanium-based biomaterials through the combination of ab-initio simulations and experimental methods. Euromat 2007, Nürnberg, Germany (2007)
Ma, D.; Raabe, D.; Roters, F.: Effects of initial orientation, sample geometry and friction on anisotropy and crystallographic orientation changes in single crystal microcompression deformation: A crystal plasticity finite element study. International workshop on small scale plasticity, Brauwald, Switzerland (2007)
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…