Stein, F.; Vogel, S. C.; Eumann, M.; Palm, M.: Determination of the crystal structure of the ε phase in the Fe–Al system by high-temperature neutron diffraction. Intermetallics 18 (1), pp. 150 - 156 (2010)
Stein, F.; Palm, M.: Re-determination of transition temperatures in the Fe–Al system by differential thermal analysis. International Journal of Materials Research 98 (7), pp. 580 - 588 (2007)
Stein, F.; Palm, M.; Sauthoff, G.: Mechanical Properties and Oxidation Behaviour of Two-Phase Iron Aluminium Alloys with Zr(Fe,Al)2 Laves Phase or Zr(Fe,Al)12 τ1 Phase. Intermetallics 13 (12), pp. 1275 - 1285 (2005)
Pöter, B.; Stein, F.; Wirth, R.; Spiegel, M.: Early stages of protective layer growth on binary iron aluminides. Zeitschrift für physikalische Chemie 219, pp. 1489 - 1503 (2005)
Stein, F.; Palm, M.; Sauthoff, G.: Structure and stability of Laves phases. Part II: Structure type variations in binary and ternary systems. Intermetallics 13 (10), pp. 1056 - 1074 (2005)
Wasilkowska, A.; Bartsch, M.; Stein, F.; Palm, M.; Sauthoff, G.; Messerschmidt, U.: Plastic deformation of Fe–Al polycrystals strengthened with Zr-containing Laves phases: Part II. Mechanical properties. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 381 (1-2), pp. 1 - 15 (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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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…