Hickel, T.; Sandschneider, N.; Friák, M.; Neugebauer, J.; Ouyang, Y.: Ab initio determination of point defects and derived diffusion properties in metals. TMS Annual meeting 2012, Orlando, FL, USA (2012)
Sandschneider, N.; Nolting, W.: A microscopic model of current-induced switching of magnetization. Theories of Correlated Electrons in Condensed Matter, Moscow, Russia (2010)
Sandschneider, N.; Hickel, T.; Neugebauer, N.: Defects and diffusion mechanisms in FeAl. Ab initio Description of Iron and Steel: Mechanical properties, 468. Wilhelm und Else Heraeus-Seminar, Ringberg, Germany (2010)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.