Nematollahi, A.; von Pezold, J.; Neugebauer, J.; Raabe, D.: Thermodynamics of the strain-induced dissolution of cementite in pearlitic structure steel: An ab-initio study. 1st Austrian-German workshop on Computational Materials Design, Kramsach, Austria (2012)
Nematollahi, A.; von Pezold, J.; Neugebauer, J.; Raabe, D.: Thermodynamic and kinetic effects of elastic strain on the decomposition of cementite in wire-draw pearlitic steel. International scientific seminar: Ab-initio description of iron and steel thermodynamics and kinetics, Ringberg Castle, Tegernsee, 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.