Toji, Y.; Matsuda, H.; Raabe, D.: Effect of Si on the acceleration of bainite transformation by pre-existing martensite. Acta Materialia 116, pp. 250 - 262 (2016)
Herbig, M.; Parra, C.D.; Lu, W.; Toji, Y.; Liebscher, C.; Li, Y.; Goto, S.; Dehm, G.; Raabe, D.: Where does the carbon atom go in steel? – Insights gained by correlative transmission electron microscopy and atom probe tomography. International Symposium on Steel Science 2017, Kyoto, Japan (2017)
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.