Wetegrove, M.; Duarte, M. J.; Taube, K.; Rohloff, M.; Gopalan, H.; Scheu, C.; Dehm, G.; Kruth, A.: Preventing Hydrogen Embrittlement: The Role of Barrier Coatings for the Hydrogen Economy, Hydrogen 4 (2 Ed.), pp. 307 - 322 (2023)
Dehm, G.; Liebscher, C.; Völker, B.; Scheu, C.: Organizer of the “IAMNano 2019 Düsseldorf” - International Workshop on Advanced In Situ Microscopies of Functional Nanomaterials and Devices. (2019)
Dehm, G.: Organizer of Mechanical Properties and Adhesion” 46th International Conference on Metallurgical Coatings & Thin Films (ICMCTF) 2019. (accepted)
Dehm, G.; Molina-Aldareguía, J. M.; Monclús, M.: Mini-symposium “Experimental Micromechanics and Nanomechanics” at the 10th European Solid Mechanics Conference (ESMC2018) (Organization). (2018)
Dehm, G.: Co-organizer of the Symposium - In situ Microscopy with Electrons, X-Rays and Scanning Probes in Materials Science at DPG Spring Meeting 2016. (2016)
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
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.