Folger, A.: The Influence of Post-Growth Heat Treatments and Etching on the Nanostructure and Properties of Rutile TiO2 Nanowires. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Gleich, S.: Investigation of Sputtered Mo2BC Hard Coatings: Correlation of Nanostructure and Mechanical Properties. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Stechmann, G.: A Study on the Microstructure Formation Mechanisms and Functional Properties of CdTe Thin Film Solar Cells Using Correlative Electron Microscopy and Atomistic Simulations. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Neddermann, P.: Martensitic Stainless Steel: Evolution of Austenite during Low Temperature Annealing and Design of Press Hardening Alloys. Dissertation, RWTH Aachen, Aachen, Germany (2016)
Zhang, J.: Microstructure design via site-specific control of recrystallization and nano-precipitation. Dissertation, RWTH Aachen, Aachen, Germany (2016)
Szczepaniak, A.: Investigation of intermetallic layer formation in dependence of process parameters during the thermal joining of aluminium with steel. Dissertation, RWTH Aachen, Aachen, Germany (2016)
Takahashi, T.: On the growth and mechanical properties of non-oxide perovskites and the spontaneous growth of soft metal nanowhiskers. Dissertation, RWTH Aachen, Aachen, Germany (2013)
Archie, F. M. F.: Nanostructured High-Mn Steels by High Pressure Torsion: Microstructure-Mechanical Property Relations. Master, Materials Chemistry, Lehrstuhl für Werkstoffchemie, RWTH Aachen, Aachen, Germany (2014)
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.