Sandlöbes, S.; Friák, M.; Dick, A.; Zaefferer, S.; Pei, Z.; Zhu, L.-F.; Sha, G.; Ringer, S.; Neugebauer, J.; Raabe, D.: Combining ab initio calculations and high resolution experiments to improve the understanding of advanced Mg-Y and Mg-RE alloys. 7th Annual Conference of the ARC Centre of Excellence for Design in Light Metals, Melbourne, VIC, Australia (2012)
Sandlöbes, S.; Friák, M.; Dick, A.; Zaefferer, S.; Pei, Z.; Neugebauer, J.; Raabe, D.: Combining ab initio calculations and high-resolution experiments to understand advanced Mg alloys. German-Korean workshop on the “Production and industrial applications of semi-finished Mg products”, Irsee, Germany (2011)
Sandlöbes, S.; Senk, D.: Automatisierung im Stahlwerk durch in-situ Ab- und Prozessgasmessung. 8. Aachener Kolloquium für Instandhaltung, Diagnose und Anlagenüberwachung (AKIDA), Eurogress, Aachen, Germany (2010)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
“Smaller is stronger” is well known in micromechanics, but the properties far from the quasi-static regime and the nominal temperatures remain unexplored. This research will bridge this gap on how materials behave under the extreme conditions of strain rate and temperature, to enhance fundamental understanding of their deformation mechanisms. The…
The precipitation of intermetallic phases from a supersaturated Co(Nb) solid solution is studied in a cooperation with the Hokkaido University of Science, Sapporo.
In this project, we employ atomistic computer simulations to study grain boundaries. Primarily, molecular dynamics simulations are used to explore their energetics and mobility in Cu- and Al-based systems in close collaboration with experimental works in the GB-CORRELATE project.
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.