Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Multi-physical alloy approaches to solid solution strengthening of Al. 1st International Conference on Material Modelling, Dortmund, Germany (2009)
Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Investigation of solid solution strengthening by density functional theory. EUROMAT 2009, Glasgow, Scotland, UK (2009)
Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Investigation of solid solution strengthening by density functional theory. 11-th National Congress on Theoretical and Applied Mechanics, Borovets, Bulgaria (2009)
Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Multi-physical alloy approaches to solid solution strengthening of Al. 15th International Conference of Strength of Materials, Dresden, Germany (2009)
Ma, D.; Raabe, D.; Roters, F.; Maaß, R.; van Swygenhoven, H.: Crystal plasticity finite element study on small scale plasticity of micropillars. 15th International Conference on the Strength of Materials (ICSMA-15), Dresden, Germany (2009)
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
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.