Morsdorf, L.; Tasan, C. C.; Ponge, D.; Raabe, D.: Lath martensite transformation, µ-plasticity and tempering reactions: potential TEM aids. Seminar at Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany (2015)
Herbig, M.; Marceau, R. K. W.; Morsdorf, L.; Raabe, D.: Spinodal Decomposition of Fe–Ni–C Martensite by Room Temperature Redistribution of Carbon Investigated by Correlative ECCI/TEM/APT. PTM 2015, Whistler, BC, Canada (2015)
Tasan, C. C.; Morsdorf, L.: In-situ characterization of martensite plasticity by high resolution microstructure and strain mapping. ICM12, Karlsruhe, Germany (2015)
Herbig, M.; Li, Y.; Morsdorf, L.; Goto, S.; Choi, P.-P.; Kirchheim, R.; Raabe, D.: Recent Advances in Understanding the Structures and Properties of Nanomaterials. Gordon Research Conference on Structural Nanomaterials, The Chinese University of Hong Kong, Hong Kong, China (2014)
Tasan, C. C.; Jeannin, O.; Barbier, D.; Morsdorf, L.; Wang, M.; Ponge, D.; Raabe, D.: In-situ characterization of martensite plasticity by high resolution microstructure and microstrain mapping. ICOMAT 2014, International Conference on Martensitic Transformations 2014, Bilbao, Spain (2014)
Morsdorf, L.: Fundamentals of ferrous low-carbon lath martensite: from the as-quenched, to tempered and deformed states. Dissertation, RWTH Aachen, Aachen, Germany (2017)
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.