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)
Krein, R.; Palm, M.; Heilmaier, M.: Characterization of microstructures, mechanical properties, and oxidation behavior of coherent A2 + L21 Fe–Al–Ti. Journal of Materials Research 24 (11), pp. 3412 - 3421 (2009)
Palm, M.: Phase equilibria in the Fe corner of the Fe–Al–Nb system between 800 and 1150°C. Journal of Alloys and Compounds 475 (1-2), pp. 173 - 177 (2009)
Palm, M.: Fe–Al materials for structural applications at high temperatures: Current research at MPIE. International Journal of Materials Research 100 (3), pp. 277 - 287 (2009)
Eumann, M.; Sauthoff, G.; Palm, M.: Phase equilibria in the Fe–Al–Mo system - Part II: Isothermal sections at 1000 and 1150 °C. Intermetallics 16 (6), pp. 834 - 846 (2008)
Krein, R.; Palm, M.: The influence of Cr and B additions on the mechanical properties and oxidation behaviour of L21-ordered Fe-Al-Ti-based alloys at high temperatures. Acta Materialia 56 (10), pp. 2400 - 2405 (2008)
Eumann, M.; Sauthoff, G.; Palm, M.: Phase equilibria in the Fe–Al–Mo system - Part I: Stability of the Laves phase Fe2Mo and isothermal section at 800 °C. Intermetallics 16 (5), pp. 706 - 716 (2008)
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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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…