Altin, A.; Wohletz, S.; Krieger, W.; Groche, P.; Erbe, A.: Effect of surface condition on the bond strength between aluminum and steel joint in cold welding. CETAS 2015, Düsseldorf, Germany (2015)
Altin, A.; Wohletz, S.; Krieger, W.; Kostka, A.; Groche, P.; Erbe, A.: Nanoscale understanding of bond formation during cold welding of aluminum and steel. 6th International Conference on Tribology in Manufacturing Processes & Joining by Plastic Deformation, Darmstadt, Germany (2014)
Altin, A.; Erbe, A.; Ritter, H.; Rohwerder, M.: Controlled release of inhibitors from composite organic coatings: A “green” way of corrosion protection. EUROCORR 2013, Estoril, Portugal (2013)
Altin, A.; Erbe, A.; Ritter, H.; Rohwerder, M.: Controlled release of inhibitors from composite organic coatings: A “green” way of corrosion protection. International Conference on self-Healing Materials, Ghent, Belgium (2013)
Vimalanandan, A.; Altin, A.; Tran, T. H.; Rohwerder, M.: Conducting Polymers for Corrosion Protection - Raspberry like shaped ICP “pigments”. Gordon Research Conference Corrosion-Aqueous, New London, NH, USA (2012)
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.
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…
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…