Lu, W.; Li, Z.; Liebscher, C.; Dehm, G.; Raabe, D.: TEM/STEM Investigations of the TRIP Effect in a Dual-Phase High-Entropy Alloy. MRS Fall Meeting, Boston, MA, USA (2018)
Su, J.; Li, Z.; Raabe, D.: Microstructural Design to Improve the Mechanical Properties of an Interstitial TRIP-TWIP High-Entropy Alloy. MRS Fall Meeting , Boston, MA, USA (2018)
Niendorf, T.; Wegener, T.; Li, Z.; Raabe, D.: On the fatigue behavior of dual-phase high-entropy alloys in the low-cycle fatigue regime. Fatique 2018, Poitiers, France (2018)
Li, Z.; Raabe, D.: Tuning Phase Transformation in Compositionally Complex Alloys for Superior Mechanical Properties. TMS 2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Oh, H. S.; Li, Z.; Kim, J. Y.; Ryu, C. W.; Meyer, A.; Tsuchiya, K.; Raabe, D.; Park, E. S.: Phase Stabilization of High Entropy Alloy under Dynamic Forcing Condition. TMS 2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Li, Z.; Raabe, D.: Designing novel high-entropy alloys towards superior properties. Frontiers in Materials Processing Applications, Research and Technology (FiMPART'2017), Bordeaux, France (2017)
Li, Z.: Designing and understanding novel high-entropy alloys towards superior properties. Talk at Universität Kassel, Institut für Werkstofftechnik, Kassel, Germany (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.