Auinger, M.; Borodin, S.; Swaminathan, S.; Rohwerder, M.: Thermodynamic Stability and Reaction Sequence for High Temperature Oxidation Processes in Steels. International Symposium “High Temperature Oxidation and Corrosion”, Zushi (Tokyo), Japan (2010)
Evers, S.; Rohwerder, M.: Localized measurement of Hydrogen amount in Metals by SKP. 6th International Conference on Diffusion in Solids and Liquids (DSL 2010), Paris, France (2010)
Rohwerder, M.: Intelligent corrosion protection by organic and by metal based nano composite coatings. Chemical Nanotechnology Talks X, Frankfurt a. M., Germany (2010)
Salgin, B.; Rohwerder, M.: Mobility of Water and Charge Carriers in Polymer/Oxide/Aluminium Alloy Interphases. M2i/DPI Project Meeting, Delft, The Netherlands (2009)
Hamou, R. F.; Biedermann, P. U.; Erbe, A.; Rohwerder, M.: Numerical simulation of probing the electric double layer by scanning electrochemical potential microscopy. International Workshops on Surface Modification for Chemical and Biochemical Sensing, Przegorzaly, Poland (2009)
Hamou, R. F.; Biedermann, P. U.; Erbe, A.; Rohwerder, M.: Screening effects in probing the double layer by scanning electrochemical potential microscopy. Comsol European Conference October 2009, Milan, Italy (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)
Hamou, R. F.; Biedermann, P. U.; Erbe, A.; Rohwerder, M.: Simulation of probing the electric double layer by scanning electrochemical potential microscopy (SECPM). 11th International Fischer Symposium on Microscopy in Electrochemistry, Benediktbeuern, Germany (2009)
Rohwerder, M.: Kelvin Probe Microscopy in Materials Science: Introduction, Overview and Future Perspectives. 23rd International Conference on Surface Modification Technologies (SMT 23), Chennai, India (2009)
Rohwerder, M.: Intelligent corrosion protection by organic coatings based on conducting polymers. Corrosion Cluster Workshop and Protection of Metals with Coatings, NIMS, Tsukuba, Japan (2009)
Swaminathan, S.; Spiegel, M.; Rohwerder, M.: Investigations on external/internal oxidation of quarternary model alloy during annealing in N2/H2: Role of dew point and dwelling time. 7th International Conference on the Microscopy of Oxidation, Chester, UK (2008)
Fenster, J. C.; Rohwerder, M.; Hassel, A. W.: Impedance-Titration: A Novel Method for Understanding the Kinetics of Corrosion in Aqueous Solutions. 59th Annual Meeting of the International Society of Electrochemistry, Sevilla, Spanien (2008)
Khan, T. R.; de la Fuenta, D.; Rohwerder, M.: Electrolytic co-deposition of SiO2 nanoparticles with zinc for improvement of corrosion protection. 59th Annual Meeting of the International Society of Electrochemistry, Seville, Spain (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…