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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.