Prabhakar, J. M.; Ostendorf, A.: Fundamental investigation of the cathodic delamination behaviour of model polymer coating on novel chromium-based coatings electrodeposited from a trivalent chromium-formate electrolyte. Dissertation, Ruhr-Universität Bochum, Fakultät für Maschinenbau (2022)
Schweinar, K.: Advancements in the understanding of Ir-based water splitting catalysts at the near-atomic scale. Dissertation, Ruhr-Universität Bochum (2021)
Dandapani, V.: Hydrogen Permeation based Potentiometry as a New Quantification Tool for Electrochemical Reactivity at Buried Interfaces and under Nanoscopic Electrolyte Layers. Dissertation, Ruhr-Universität Bochum, Bochum, Germany (2017)
Toparli, C.: Passivity and passivity breakdown on copper: In situ and operando observation of surface oxides. Dissertation, Ruhr-Universität Bochum, Fakultät Maschinenbau, Bochum, Germany (2017)
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
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.