Herbig, M.; Li, Y.; Choi, P.: Atomic Analysis of Concentration Changes at Interfaces by Atom Probe Tomography. SFB 761 Doktorandenseminar, RWTH Aachen, Germany (2011)
Kühbach, M.; Breen, A. J.; Herbig, M.; Gault, B.; Raabe, D.: Building a Library of Simulated Atom Probe Data for Different Crystal Structures and Pillar Orientations Using TAPSim. APT&M 2018 International Conference on Atom-Probe Tomography & Microscopy, Washington, DC, USA (2018)
Herbig, M.; Choi, P.; Raabe, D.: A Sample Holder System that Enables Sophisticated TEM Analysis of APT Tips. International Field Emission Symposium 2012, Tuscaloosa, AL, USA (2012)
Herbig, M.: Hüftimplantate: Ein werkstoffwissenschaftlicher Blick auf Geschichte, Möglichkeiten und Limitationen. Habilitation, RWTH Aachen University (2021)
Parra Moran, C.: Atomic scale analysis of grain boundary segregation in pearlitic steel. Master, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador (2017)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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…
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.