Vogel, S. C.; Eumann, M.; Palm, M.; Stein, F.: Investigation of the crystallographic structure of the ε phase in the Fe–Al system by high-temperature neutron diffraction. American Conference on Neutron Scattering (ACNS 2008), Santa Fe, New Mexico, USA (2008)
Stein, F.: Composition dependence of nanohardness and Young's modulus in diffusion couples containing Laves phases. Workshop "The Nature of Laves Phases X", Dresden, Germany (2008)
Stein, F.; Frommeyer, G.; Schneider, S. M.: Processing of eutectic NiAl–Cr and NiAl–Re alloys under microgravity. Meeting "TEMPUS Parabolic Flight September 2007", Bonn, Germany (2008)
Prymak, O.; Stein, F.; Frommeyer, G.; Raabe, D.: Phase equilibria in the Nb–Cr–Al system at 1150, 1300 and 1450 °C. Workshop "The Nature of Laves Phases IX", Stuttgart, Germany (2007)
Prymak, O.; Stein, F.; Palm, M.; Frommeyer, G.; Raabe, D.: Konstitutionsuntersuchungen im System Nb-Cr-Al: Erste Ergebnisse und weitere Planungen. Workshop: The Nature of Laves Phases VII, MPI für Metallforschung Stuttgart, Germany (2006)
Stein, F.; Frommeyer, G.; Schneider, S. M.: Iron-Silicon Alloys with 3.5, 4.5 and 5.5 wt.% Si Processed under Microgravity. TEMPUS Parabolic Airplane Flight 2006 Meeting, DLR Bonn, Germany (2006)
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)
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.