Herbig, M.; Raabe, D.; Li, Y.; Choi, P.; Zaefferer, S.; Goto, S.: High Throughput Quantification of Grain Boundary Segregation by Correlative TEM and APT. TMS 2014, Solid-State Interfaces III Symposium, San Diego, CA, USA (2014)
Herbig, M.; Choi, P.-P.; Raabe, D.: Atom Probe Tomography and Correlative TEM/APT at the MPIE. Mini-Symposium Atom Probe Tomography, National APT Facility Eindhoven, TU Delft, Delft, The Netherlands (2014)
Herbig, M.; Raabe, D.; Li, Y.; Choi, P.-P.; Zaefferer, S.; Goto, S.: High Throughput Quantification of Grain Boundary Segregation by Correlative Transmission Electron Microscopy and Atom Probe Tomography. International Conference on Atom Probe Tomography & Microscopy 2014, Stuttgart, Germany (2014)
Choi, P.: Characterization of κ-carbide precipitates in austenitic Fe–Mn–Al–C steels using atom probe tomography. Thermec 2013, Las Vegas, NV, USA (2013)
Herbig, M.; Raabe, D.; Li, Y. J.; Choi, P.; Zaefferer, S.; Goto, S.: Quantification of Grain Boundary Segregation in Nanocrystalline Material. Seminar at Department Microstructure Physics and Alloy Design, MPI für Eisenforschung, Düsseldorf, Germany (2013)
Herbig, M.; Choi, P.; Raabe, D.: Combining Structural and Chemical Information on the nm Scale by Correlative TEM and APT Characterization. European Atom Probe Workshop 2013 at ETH Zürich, Zürich, Switzerland (2013)
Herbig, M.; Choi, P.; Raabe, D.: Combining Structural and Chemical Information on the nm Scale by Correlative TEM and APT Characterization. Euromat 2013, Sevilla, Spain (2013)
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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…