Srikakulapu, K.; Qin, Y.; Sreekala, L.; Morsdorf, L.; Herbig, M.: On the decomposition resistance of carbonitride precipitates during high-pressure torsion in X30CrMoN15-1 bearing steel. High Nitrogen Steel conference, HNS 2021, online, Shanghai, China (2021)
Qin, Y.; Mayweg, D.; Tung, P.-Y.; Pippan, R.; Herbig, M.: Mechanism of cementite decomposition in 100Cr6 bearing steels during high pressure torsion. MSE Congress 2020, virtual, Sankt Augustin, Germany (2020)
Mayweg, D.; Morsdorf, L.; Wu, X.; Herbig, M.: The role of carbon in the white etching crack phenomenon in bearing steels. MSE Congress 2020, virtual, Sankt Augustin, Germany (2020)
Herbig, M.: Joint Nanoscale Structural and Chemical Characterization by Correlative Atom Probe Tomography and Transmission Electron Microscopy. Joint Workshop on Nano-Characterisation (4TU.HTM / M2i), Utrecht, The Netherlands (2019)
Herbig, M.: Atomare Einsichten in Struktur und Zusammensetzung von Stählen durch korrelative Elektronenmikroskopie / Atomsondentomographie. 25. Werkstoffkolloquium des Technischen Beirats, Hannover, Germany (2017)
Herbig, M.; Parra, C.D.; Lu, W.; Toji, Y.; Liebscher, C.; Li, Y.; Goto, S.; Dehm, G.; Raabe, D.: Where does the carbon atom go in steel? – Insights gained by correlative transmission electron microscopy and atom probe tomography. International Symposium on Steel Science 2017, Kyoto, Japan (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
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.