Li, Y. J.; Choi, P.; Goto, S.; Borchers, C.; Raabe, D.; Kirchheim, R.: Evolution of strength and microstructure during annealing of heavily cold-drawn 6.3 GPa hypereutectoid pearlitic steel wire. 53rd International Field Emission Symposium (IFES), Tascaloosa, AL, USA (2012)
Li, Y. J.; Choi, P.; Borchers, C.; Chen, Y.Z.; Goto, S.; Raabe, D.; Kirchheim, R.: Atom Probe Tomography characterization of heavily cold drawn pearlitic steel wire. 52nd International Field Emission Symposium (IFES), Sydney, Australia (2010)
Djaziri, S.; Li, Y.; Goto, S.; Kirchlechner, C.; Raabe, D.; Dehm, G.: Microstructural characterization of cold-drawn pearlitic steel wires at the nanometer scale. The Thin Film & Small Scale Mechanical Behavior Gordon Research Conference, Waltham, MA, USA (2014)
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
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.
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…