Salgin, B.; Rohwerder, M.: Mobility of Water and Charge Carriers in Polymer/Oxide/Aluminium Alloy Interphases. M2i/DPI Project Meeting, Delft, The Netherlands (2009)
Salgin, B.; Rohwerder, M.: A New Approach to Determine Ion Mobility Coefficients for Delamination Scenarios. electrochem09 and 50th Corrosion Science Symposium, Manchester, UK (2009)
Salgin, B.; Rohwerder, M.: A New Approach to Determine Ion Mobility Coefficients for Delamination Scenarios. 60th Annual Meeting of the International Meeting of the International Society of Electrochemistry, Beijing, China (2009)
Salgin, B.; Rohwerder, M.: Effects of Semiconducting Properties of Surface Oxide on the Delamination at the Polymer/Zinc Interface. SurMat Seminar, Kleve, Germany (2008)
Salgin, B.; Rohwerder, M.: Mobility of Water and Charge Carriers in Polymer/Oxide/Aluminium Alloy Interphases. M2i Conference 2011, Noordwijkerhout, The Netherlands (2011)
Salgin, B.; Rohwerder, M.: Scanning Kelvin Probe (SKP) as a tool for monitoring ion mobility on insulators. M2i Conference 2009, Noordwijkerhout, The Netherlands (2009)
Salgin, B.; Rohwerder, M.: Effects of the Semiconducting Properties of Surface Oxide on the Delamination at the Polymer/Metal Interface. 2nd International IMPRS-SurMat Workshop, Bochum, Germany (2008)
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…