Elhami, N.-N.; Zaefferer, S.; Thomas, I.; Hofmann, H.: Observation of the crystallographic defect structure in lightly deformed TWIP steel by means of electron channeling contrast imaging (ECCI). 1st International Conference on High Manganese Steels (HMnS2011), Seoul, South Korea (2011)
Fanta, A. B.; Zaefferer, S.; Thomas, I.; Raabe, D.: Relationship Between Microstructure and Texture Evolution during Cold Deformation of TWIP-Steels. 15 th International Conference on the Texture of Materials (ICOTOM 15), Pittsburgh, PA, USA (2008)
Thomas, I.; Zaefferer, S.; Friedel, F.; Raabe, D.: Orientation dependent growth behaviour of subgrain structures in IF steel. 2nd International Joint Conference on Recrystallization and Grain Growth, Annecy, France (2004)
Thomas, I.: Untersuchung metallphysikalischer und messtechnischer Grundlagen zur Rekristallisation und Erholung mikrolegierter IF Stähle. Dissertation, RWTH Aachen, Aachen, Germany (2008)
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.