Hieke, S. W.; Dehm, G.; Scheu, C.: Temperature induced faceted hole formation in epitaxial Al thin films on sapphire. 8th International Conference on High Temperature Capillarity (HTC-2015), Bad Herrenalb, Germany (2015)
Folger, A.; Wisnet, A.; Scheu, C.: Transmission electron microscopic characterization of TiO2/NbxOy core-shell nanowires. Autumn School on Microstructural Characterization and Modelling of Thin-Film Solar Cells, Werder, Germany (2014)
Frank, A.; Wochnik, A. S.; Betzler, S. B.; Scheu, C.: Copper indium disulfide films synthesized with L-cysteine. Autumn School on Microstructural Characterization and Modelling of Thin-Film Solar Cells, Werder, Potsdam, Germany (2014)
Hieke, S. W.; Dehm, G.; Scheu, C.: Solid state dewetting phenomena of epitaxial Al thin films on sapphire (α-Al2O3). 2nd International Multidisplinary Microscopy Congress (InterM 2014), Oludeniz, Fethiye, Turkey (2014)
Gleich, S.; Heinzl, C.; Ossiander, T.; Perchthaler, M.; Scheu, C.: Investigation of high-temperature polymer electrolyte membrane fuel cells by electron microscopy methods. CENS Workshop “Nanosciences: Great Adventures on Small Scales”, Venice, Italy (2013)
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.
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…
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.
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…