Wang, Z.: Investigation of crystallographic character and molten-salt-corrosion properties of grain boundaries in a stainless steel using EBSD and ab-initio calculations. Dissertation, Ruhr-Universität Bochum, Bochum, Germany (2017)
Elhami, N. N.: Influence of strain path changes during cup drawing on the twinning activity in TWIP steels investigated by ECCI. 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)
Ram, F.: The Kikuchi bandlet method for the intensity analysis of the Electron Backscatter Kikuchi Diffraction Patterns. Dissertation, RWTH Aachen, Aachen, Germany (2015)
Schemmann, L.: The inheritance of different microstructures found after hot rolling on the properties of a completely annealed dual phase steel. Dissertation, Fakultät für Georessourcen und Materialtechnik, RWTH Aachen, Aachen, Germany (2014)
Jäpel, T.: Feasibility study on local elastic strain measurements with an EBSD pattern cross correlation method in elastic-plastically deforming material. Dissertation, RWTH Aachen, Aachen, Germany (2014)
Elhami, N.-N.: Schädigungsmechanismen und Entwicklung der Mikrostruktur eines Al-legierten TRIP-Stahls im Laufe der Verformung. Diploma, RWTH Aachen, Aachen, Germany (2008)
Shan, Y.: Investigation on the Influence of Hydrogen on Dislocation Formation during Nanoindentation in TWIP Steels. Master, RWTH Aachen, Aachen, Germany (2018)
Kuo, J. C.; Zaefferer, S.; Raabe, D.: Experimental investigation of the deformation behavior of aluminium-bicrystals. MPI für Eisenforschung GmbH, Düsseldorf, Germany (2004)
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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.