Friák, M.; Tytko, D.; Holec, D.; Choi, P.-P.; Eisenlohr, P.; Raabe, D.; Neugebauer, J.: Synergy of atom-probe structural data and quantum-mechanical calculations in a theory-guided design of extreme-stiffness superlattices containing metastable phases. New Journal of Physics 17 (9), 093004 (2015)
Kim, J.-H.; Kim, B. K.; Kim, D.-I.; Choi, P.-P.; Raabe, D.; Yi, K.-W.: The role of grain boundaries in the initial oxidation behavior of austenitic stainless steel containing alloyed Cu at 700 °C for advanced thermal power plant applications. Corrosion Science 96, pp. 52 - 66 (2015)
Herbig, M.; Choi, P.-P.; Raabe, D.: Combining structural and chemical information at the nanometer scale by correlative transmission electron microscopy and atom probe tomography. Ultramicroscopy 153, pp. 32 - 39 (2015)
Tytko, D.; Choi, P.-P.; Raabe, D.: Thermal dissolution mechanisms of AlN/CrN hard coating superlattices studied by atom probe tomography and transmission electron microscopy. Acta Materialia 85, pp. 32 - 41 (2015)
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…