Mayweg, D.: Microstructural characterization of white etching cracks in 100Cr6 bearing steel with emphasis on the role of carbon. Dissertation, RWTH Aachen University (2021)
Schweinar, K.: Advancements in the understanding of Ir-based water splitting catalysts at the near-atomic scale. Dissertation, Ruhr-Universität Bochum (2021)
Varanasi, R. S.: Mechanisms of refinement and deformation of novel ultrafine-grained medium manganese steels with improved mechanical properties. Dissertation, Ruhr-Universität Bochum (2021)
Keuter, P.: Design of materials with anomalous thermophysical properties and desorption-assisted phase formation of intermetallic thin films. Dissertation, RWTH Aachen University (2020)
Pei, R.: Microstructural Relationships of Strength and Ductility in a Newly Developed Mg–Al–Zn Alloy for Potential Automotive Applications. Dissertation, RWTH Aachen University (2020)
Pei, R.: Microstructural Relationships of Strength and Ductility in a Newly Developed Mg–Al–Zn Alloy for Po-tential Automotive Applications. Dissertation, RWTH Aachen University (2020)
Kürnsteiner, P.: Precipitation Reactions During the Intrinsic Heat Treatment of Laser Additive Manufacturing. Dissertation, RWTH Aachen University (2019)
Dutta, A.: Deformation behaviour and texture memory effect of multiphase nano-laminate medium manganese steels. Dissertation, RWTH Aachen University (2019)
Hariharan, A.: On the interfacial defect formation mechanism during laser additive manufac-turing of polycrystalline superalloys. Dissertation, Ruhr-Universität Bochum (2019)
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.