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)
Raabe, D.; Gault, B.; Yao, M.; Scheu, C.; Liebscher, C.; Herbig, M.: Correlated and simulated electron microscopy and atom probe tomography. Workshop on Possibilities and Limitations of Quantitative Materials Modeling and Characterization 2017, Bernkastel, Germany (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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
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.