Belde, M. M.; Springer, H.; Inden, G.; Raabe, D.: Multiphase microstructures via confined precipitation and dissolution of vessel phases: Example of austenite in martensitic steel. Acta Materialia 86, pp. 1 - 14 (2015)
Kuzmina, M.; Ponge, D.; Raabe, D.: Grain boundary segregation engineering and austenite reversion turn embrittlement into toughness: Example of a 9 wt.% medium Mn steel. Acta Materialia 86, pp. 182 - 192 (2015)
Ma, D.; Friák, M.; von Pezold, J.; Raabe, D.; Neugebauer, J.: Computationally efficient and quantitatively accurate multiscale simulation of solid-solution strengthening by ab initio calculation. Acta Materialia 85, pp. 53 - 66 (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)
Li, Y.; Ponge, D.; Choi, P.-P.; Raabe, D.: Segregation of boron at prior austenite grain boundaries in a quenched martensitic steel studied by atom probe tomography. Scripta Materialia 96, pp. 13 - 16 (2015)
Kim, J.-K.; Sandlöbes, S.; Raabe, D.: On the room temperature deformation mechanisms of a Mg–Y–Zn alloy with long period stacking ordered structures. Acta Materialia 82, pp. 414 - 423 (2015)
Springer, H.; Tasan, C. C.; Raabe, D.: A novel roll-bonding methodology for the cross-scale analysis of phase properties and interactions in multiphase structural materials. International Journal of Materials Research 106 (1), pp. 3 - 14 (2015)
Jägle, E. A.; Choi, P.-P.; Raabe, D.: The maximum separation cluster analysis algorithm for atom-probe tomography: Parameter determination and accuracy. Microscopy and Microanalysis 20 (6), pp. 1662 - 1671 (2014)
Scientists at the Max Planck Institute for Sustainable Materials have developed a carbon-free, energy-saving method to extract nickel for batteries, magnets and stainless steel.
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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