Pizzagalli, L.; Dehm, G.; Thomas, O.: Structure and dynamics V: Mechanical properties at small scales. Condensed Matter in Paris: Mini-colloquium 32, Paris, France (2014)
Dehm, G.: From idealized bi-crystals towards applied polycrystals: Plastic deformation in small dimensions. 2013 MRS Fall Meeting, Boston, MA, USA (2013)
Dehm, G.: Structure and Micromechanics of Materials. Materialwissenschaftliches Kolloquium ICAMS und Institut für Werkstoffe, RUB, Bochum, Germany (2013)
Dehm, G.: Probing deformation phenomena at small length scales. ECI on Nanomechanical Testing in Materials Research and Development IV, Olhão, Portugal (2013)
Dehm, G.: Atomic resolution interface study of VN and Cu films on MgO using Cs corrected TEM. Microscopy Conference MC 2013, Regensburg, Germany (2013)
Dehm, G.: Struktur und Nano-/Mikromechanik von Materialien. Vorstandssitzung des Stahlinstituts VDEh und der Wirtschaftsvereinigung Stahl, Düsseldorf, Germany (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
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.
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…