Rohwerder, M.: Wasserstoff in Metallen: neue Messverfahren zum Nachweis mit hoher räumlicher Auflösung. Habilitation, Ruhr-Universität Bochum, Bochum, Germany (2016)
Rohwerder, M.; Vogerl, A.; Jarosik, A.; Muhr, A.; Norden, M.; Bordignon, M.; Vanden Eynde, X.: Novel Annealing Procedures for Improving Hot Dip Galvanizing of High Strength Steels. (2010)
Rohwerder, M.; Allély, K. O.; Bendick, M.; Altgassen, C.; Conejero, O.; Tomandl, A.; Fernandes, J. S.; Simoes, A.; Chassagne, J.: Self-Healing at Cut-Edge of Coil Coated Galvanized Steel. (2009)
Hübel, K.; Rohwerder, M.; Scheu, C.; Todorova, M.: Organizer of the workshop “Status and Future Challenges in Characterisation of Interfaces for Electrochemical Applications - Part 1” at the MPIE. (2016)
Rohwerder, M.: Symposium X1 - Electron Transfer Reactions at Organic/Metal Interfaces: From Molecular Monolayer Modified Electrodes to Buried Polymer Metal Interfaces. (2006)
Mukherjee, T.; Rohwerder, M.: Hydrogen-Bonding Interactions in Water-Ethanol Solutions and the Effect of Adding KCl. ChemRxiv: the Preprint Server for Chemistry 2026, 0814 (2026)
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…