Gomell, L.: Advancing the understanding of the microstructure-property relationship in non-toxic and cost-effective thermoelectric Heusler compounds. Dissertation, Fakultät für Georessourcen und Materialtechnik der RWTH Aachen, Germany (2022)
Yilmaz, C.: Influence of Processing Parameters, Crystallography and Chemistry of Defects on the Microstructure and Texture Evolution in Grain-Oriented Electrical Steels. Dissertation, RWTH Aachen, Germany (2022)
Prithiv, T. S.: Grain boundary segregation of boron and carbon and their local chemical effects on the phase transformations in steels. Dissertation, Faculty of Georesources and Materials Engineering of the RWTH Aachen, Germany (2021)
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
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.
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.