Horiuchi, T.; Stein, F.: Precipitation Behavior of Co7Nb2 from Supersaturated Co Solid Solution in Co–Nb Binary System. Intermetallics 2015, Educational Center Kloster Banz, Bad Staffelstein, Germany (2015)
Luo, W.; Kirchlechner, C.; Dehm, G.; Stein, F.: A New Method to Study the Composition Dependence of Mechanical Properties of Laves Phases. Intermetallics 2015, Educational Center Kloster Banz, Bad Staffelstein, Germany (2015)
Stein, F.; Vogel, S. C.; Eumann, M.; Palm, M.: In-situ Neutron Diffraction Experiments on the Effect of Mo on the Structure of the High-Temperature ε Phase of the Fe–Al System. 5th Discussion Meeting on the Development of Innovative Iron Aluminium Alloys (FEAL 2009), Prague, Czech Republic (2009)
Zaefferer, S.; Ishikawa, S.; Stein, F.: Distinction of different Laves phase types by EBSD in a TiCr diffusion couple: Robust detection of subtle differences in EBSD patterns. Electron Backscatter Diffraction Meeting, RMS Conference, Sheffield, UK (2008)
Liapina, T.; Spiegel, M.; Stein, F.: Short-term oxidation of Fe–Al: Effect of ternary elements and Al content. 4th Discussion Meeting on the Development of Innovative Iron Aluminium Alloys, Interlaken, Switzerland (2007)
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. 4th Discussion Meeting of the Development of Innovative Iron Aluminium Alloys, Interlaken, Switzerland (2007)
Palm, M.; Risanti, D.-D.; Stallybrass, C.; Stein, F.; Sauthoff, G.: Strengthening of Corrosion-Resistant Fe–Al Alloys Through Intermetallic Precipitates. Discussion Meeting on the Development of Innovative Iron Aluminium Alloys, Düsseldorf, Germany (2004)
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…