Grabowski, B.: Modern materials design from first-principles: Recent progress and future prospects. Seminar, Imperial College London, London, UK (2015)
Grabowski, B.: Ab initio thermodynamics of the CoCrFeMnNi high entropy alloy: Importance of entropy contributions beyond the configurational one. ICAMS Seminar, Ruhr-University Bochum, Bochum, Germany (2015)
Grabowski, B.: Random phase approximation up to the melting point: The impact of anharmonicity and non-local many-body effects on the thermodynamics of Au. MISIS Workshop, Moscow, Russia (2015)
Körmann, F.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Temperature-dependent coupling of atomic and magnetic degree of freedom from first-principles. Electronic Structure Theory for the Accelerated Design of Structural Materials, Moscow, Russia (2015)
Grabowski, B.; Wippermann, S. M.; Glensk, A.; Hickel, T.; Neugebauer, J.: Random phase approximation up to the melting point: Impact of anharmonicity and nonlocal many-body effects on the thermodynamics of Au. DPG Spring Meeting 2015, Berlin, Germany (2015)
Hickel, T.; Glensk, A.; Grabowski, B.; Körmann, F.; Neugebauer, J.: Thermodynamics of materials up to the melting point: The role of anharmonicities. Asia Sweden Meeting on Understanding Functional Materials from Lattice dynamics, Guwahati, India (2014)
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.