Biedermann, P. U.; Flechtner, K.-D.: Towards a Thermodynamic Theory of Electrochemical Reactions in Aqueous Media. A DFT Study of the Intermediates of Oxygen Reduction. 46th Symposium on Theoretical Chemistry, STC2010, Münster, Germany (2010)
Biedermann, P. U.; Flechtner, K.-D.: Theoretical Insights into the Mechanism of the Oxygen Reduction Reaction. Electrochemistry 2010, Ruhr-Universität Bochum, Bochum, Germany (2010)
Nayak, S.; Biedermann, P. U.; Erbe, A.: Spectroscopic Investigation of the Oxygen Reduction Reaction (ORR) on Semiconductor Surfaces. Electrochemistry 2010 - From microscopic understanding to global impact, Bochum, Germany (2010)
Nayak, S.; Biedermann, P. U.; Erbe, A.: Electrochemical oxygen reduction on semiconductor electrodes. 109th Annual meeting of the German Bunsen Society of Physical Chemistry (Bunsentagung), Bielefeld, Germany (2010)
Hamou, R. F.; Biedermann, P. U.; Rohwerder, M.; Blumenau, A. T.: FEM Simulation of the Scanning Electrochemical Potential Microscopy (SECPM). 2nd IMPRS-SurMat Workshop in Surface and Interface Engineering in Advanced Materials, Ruhr-Universität Bochum, Bochum, Germany (2008)
Torres, E.; Biedermann, P. U.; Blumenau, A. T.: A DFT study of Alkanethiol adsorption sites on Au(111) surfaces. 2nd IMPRS-SurMat Workshop in Surface and Interface Engineering in Advanced Materials, Ruhr-Universität Bochum, Bochum, Germany (2008)
Biedermann, P. U.; Torres, E.; Laaboudi, L.; Isik-Uppenkamp, S.; Rohwerder, M.; Blumenau, A. T.: Cathodic Delamination by a Combined Computational and Experimental Approach: The Aklylthiol/Gold Model System. Multiscale Material Modeling of Condensed Matter, MMM2007, St. Feliu de Guixols, Spain (2007)
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
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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.