Dutta, B.; Körmann, F.; Dey, P.; Hickel, T.; Neugebauer, J.: Ab-initio based prediction of chemical trends for phase transitions in magnetic shape memory alloys. Weekly Seminar, Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-Universität Bochum, Bochum, Germany (2013)
Bleskov, I.; Körmann, F.; Hickel, T.; Neugebauer, J.: Impact of Magnetism on Thermodynamic Properties of Iron. International Symposium “Frontiers In Electronic Structure Theory And Multi Scale Modeling” (FEST-VEK), Moscow, Russia (2013)
Körmann, F.; Dick, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: The influence of magnetic excitations on the phase stability of metals and steels. Seminar Talk at Institute for Pure and Applied Math, UCLA, University of California, Los Angeles, CA, USA (2012)
Palumbo, M.; Fries, S. G.; Hammerschmidt, T.; Körmann, F.; Hickel, T.: SAPIENS thermophysical database for pure elements: DFT and experiments. 18th Symposium on Thermophysical Properties, Boulder, CO, USA (2012)
Körmann, F.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Advancing ab initio methods to finite temperatures: The opening of new routes in materials design. Seminar Talk at Institute on Quantum Materials Science, Yekaterinburg, Russia (2012)
Dick, A.; Körmann, F.; Hickel, T.; Neugebauer, J.: Thermodynamic properties of cementite including magnetic, vibronic, and electronic excitations from ab initio. TMS Annual meeting 2012, Orlando, FL, USA (2012)
Palumbo, M.; Fries, S. G.; Hammerschmidt, T.; Drautz, R.; Körmann, F.; Hickel, T.; Neugebauer, J.: SAPIENS, a DFT and experimental based thermophysical database for pure elements. DPG Frühjahrstagung 2012, Berlin, Germany (2012)
Körmann, F.; Dick, A.; Hickel, T.; Neugebauer, J.: The influence of magnetic excitations on the phase stability of metals and steels. ADIS2012: Thermodynamics and Kinetics, Ringberg, Germany (2012)
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.