Herbig, M.: Spatially correlated electron microscopy and atom probe tomography: Current possibilities and future perspectives. Scripta Materialia 148, pp. 98 - 105 (2018)
Li, Y.; Herbig, M.; Goto, S.; Raabe, D.: Atomic scale characterization of white etching area and its adjacent matrix in a martensitic 100Cr6 bearing steel. Materials Characterization 123, pp. 349 - 353 (2017)
Li, Y.; Herbig, M.; Goto, S.; Raabe, D.: Formation of nanosized grain structure in martensitic 100Cr6 bearing steels upon rolling contact loading studied by atom probe tomography. Materials Science and Technology 32 (11), pp. 1100 - 1105 (2016)
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 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…