Haghighat, S. M. H.; Welsch, E. D.; Gutiérrez-Urrutia, I.; Roters, F.; Raabe, D.: Mesoscale modeling of dislocation mechanisms and the effect of nano-sized carbide morphology on the strengthening of advanced lightweight high-Mn steels. MMM2014, 7th International Conference on Multiscale Materials Modeling
, Berkeley, CA, USA (2014)
Haghighat, S. M. H.; Welsch, E. D.; Gutiérrez-Urrutia, I.; Raabe, D.: Alloy design of advanced lightweight high-Mn steels by combined TEM and discrete dislocation dynamics simulations. 2nd International Conference on High Manganese Steels, Aachen, Germany (2014)
Welsch, E. D.; Haghighat, S. M. H.; Gutiérrez-Urrutia, I.; Raabe, D.: Investigation of nano-sized kappa carbide distribution in advanced austenitic lightweight high-Mn steels by coupled TEM and DDD simulations: Strengthening and dislocation-based mechanisms. 2nd International Conference on High Manganese Steels, 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
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.