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)
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…