Peter, N. J.; Liebscher, C.; Kirchlechner, C.; Dehm, G.: Ag segregation induced nanofaceting transition of an asymmetric tilt grain boundary in Cu and its impact on plastic deformation mechanisms. PICO 2019, Vaals, The Netherlands (2019)
Peter, N. J.; Kirchlechner, C.; Liebscher, C.; Dehm, G.: Effect of the atomistic grain boundary structure on dislocation interaction in copper. Gordon Research Conference (GRC) 2016, Thin Film & Small Scale Mechanical Behavior
, Lewiston, ME, USA (2016)
Peter, N. J.; Kirchlechner, C.; Liebscher, C.; Dehm, G.: Beam induced atomic migration at Ag containing nanofacets at an asymmetric Cu grain boundary. European Microscopy Congress (EMC) 2016
, Lyon, France (2016)
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.