Genchev, G.; Cox, K.; Sarfraz, A.; Bosch, C.; Spiegel, M.; Erbe, A.: Sour corrosion – Investigation of iron sulfide layer growth in saturated H2S solutions. In: Proceedings of the European Corrosion Congress EUROCORR. European Corrosion Congress EUROCORR 2014, Pisa, Italy, September 08, 2014 - September 12, 2014. (2014)
Genchev, G.; Cox, K.; Sarfraz, A.; Bosch, C.; Spiegel, M.; Erbe, A.: Sour corrosion – Investigation of anodic iron sulfide layer growth in saturated H2S saline solutions. Gordon Research Conference-Aqueous Corrosion, New London, NH, USA (2014)
Genchev, G.; Cox, K.; Sarfraz, A.; Bosch, C.; Spiegel, M.; Erbe, A.: Sour corrosion – Investigation of anodic iron sulfide layer growth in saturated H2S saline solutions. Gordon Research Seminar-Aqueous Corrosion, New London, NH, USA (2014)
Cox, K.: Elektrochemische Untersuchung von Eisen im Schwefelwasserstoff gesättigten Elektrolyten. Bachelor, Faculty of Chemistry, Niederrhein University of Applied Sciences (Hochschule Niederrhein), Krefeld, Germany (2013)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.
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.