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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.