Kürnsteiner, P.; Wilms, M. B.; Weisheit, A.; Jägle, E. A.; Raabe, D.: In-process precipitation strengthening in Al–Sc during Laser Metal Deposition by exploiting the Intrinsic Heat Treatment. Alloys for Additive Manufacturing Symposium, Zürich, Switzerland (2017)
Rusitzka, A. K.; Stephenson, L.; Gremer, L.; Raabe, D.; Willbold, D.; Gault, B.: Getting insights to Alzheimer‘s disease by atom probe tomography. 6th International caesar conference, Overcoming Barriers — atomic-resolution and beyond: advances in molecular electron microscopy, Bonn, Germany (2017)
Konijnenberg, P. J.; An, D.; Stechmann, G.; Zaefferer, S.; Raabe, D.: Recent Developments in the Analysis of Microstructures by 3D-EBSD. Symposium: 3D materials characterization at all length scales and its applications to iron and steel, Düsseldorf, Germany (2017)
Peng, Z.; Gault, B.; Raabe, D.: On the Multiple Event Detection in Atom Probe Tomography. Microscopy & Microanalysis 2017 Conference, St. Louis, MO, USA (2017)
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)
Liu, C.; Diehl, M.; Shanthraj, P.; Roters, F.; Raabe, D.; Sandlöbes, S.; Dong, J.: An integrated crystal plasticity-phase field approach to locally predict twin formation in magnesium. DGM Meeting, "Herausforderungen bei der skalenübergreifenden Modellierung von Werkstoffen ", Regensburg, Germany (2017)
Roters, F.; Wong, S. L.; Shanthraj, P.; Diehl, M.; Raabe, D.: Thermo mechanically coupled simulation of high manganese TRIP/TWIP Steel. 5th International Conference on Material Modeling, ICMM 5, Rome, Italy (2017)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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.