Stein, F.: The Nature of Laves Phases – A Critical Assessment of the Current Knowledge on Structure and Stability of Laves Phases. Workshop "The Nature of Laves Phases VI, MPI für Chemische Physik fester Stoffe, Dresden, Germany (2006)
Palm, M.; Schneider, A.; Stein, F.; Sauthoff, G.: Strengthening of Fe–Al-Based Alloys for High-Temperature Applications. 3rd Disc.Meeting on the Development of Innovative Iron Aluminium Alloys, Mettmann-Düsseldorf, Germany (2006)
Spiegel, M.; Stein, F.; Pöter, B.: Initial Stages of Oxide Growth on Fe–Al Alloys. 3rd Disc.Meeting on the Development of Innovative Iron Aluminium Alloys, Mettmann-Düsseldorf, Germany (2006)
Stein, F.; Palm, M.: DTA Studies on the Fe–Al Phase Diagram. 3rd Disc.Meeting on the Development of Innovative Iron Aluminium Alloys, Mettmann-Düsseldorf, Germany (2006)
Palm, M.; Schneider, A.; Stein, F.; Sauthoff, G.: Iron-Aluminium-Base Alloys for Structural Applications at High Temperatures: Needs and Prospects. EUROMAT 2005, Prague, Czech Republic (2005)
Stein, F.; Dovbenko, O. I.; Palm, M.: Experimental Investigations of Structure Type Variations of Laves Phases. International Conference on "Modern Materials Science: Achievements and Problems", Kiev, Ukraine (2005)
Stein, F.; Dovbenko, O. I.; Palm, M.: Phase Relations between Laves Phases in Transition Metal Systems - Case Studies: Co–Nb, Al–Co–Nb, Cr–Ti, Fe–Zr, Al–Fe–Zr. EUROMAT 2005, Prague, Czech Republic (2005)
Dovbenko, O. I.; Palm, M.; Stein, F.: Phase Equilibria in the Al–Co–Nb Ternary System in the Vicinity of the Laves Phases. CALPHAD XXXIV, Maastricht, The Netherlands (2005)
Stein, F.; Frommeyer, G.: Untersuchung des Erstarrungsgefüges einer unter Schwerelosigkeit erschmolzenen intermetallischen TiAl-Legierung. Workshop "Entwicklung der Basis - Erkennen der Perspektiven", Materialwissenschaften und mg-Forschung, MPI für Eisenforschung, Düsseldorf, Germany (2005)
Dovbenko, O. I.; Palm, M.; Stein, F.: Investigation of the Phase Equilibria in the Al–Co–Nb System. Preliminary Results. International Workshop "Laves Phases IV", MPI für Eisenforschung, Düsseldorf, Germany (2005)
Dovbenko, O. I.; Palm, M.; Stein, F.: Investigation of the Phase Equilibria in the Al–Co–Nb System using Liquid-Solid Diffusion Couples. Preliminary Results. COST 535 Diffusion Couple Workshop, MPI für Eisenforschung, Düsseldorf, Germany (2004)
Stein, F.; Jiang, D.; Palm, M.; Sauthoff, G.: Laves Phase Polytypism in the Co–Nb System. TOFA 2004 - Discussion Meeting on Thermodynamics of Alloys, Wien, Austria (2004)
Stein, F.; Schneider, A.; Frommeyer, G.: Quaternary Fe3Al-Based Alloys with Transition Metals: Effect of Alloying Additions on the Order-Disorder Transitions and the Mechanical Behaviour. Discussion Meeting on the Development of Innovative Iron Aluminium Alloys, MPI für Eisenforschung GmbH, Düsseldorf, Germany (2004)
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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
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.