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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.