Dehm, G.: Resolving the mechanical performance of materials in microelectronic components with µm spatial resolution. FIMPART - Frontiers in Materials Processing Applications, Research and Technology, Bordeaux, France (2017)
Duarte, M. J.; Fang, X.; Brinckmann, S.; Dehm, G.: In-situ nanoindentation of hydrogen bcc Fe–Cr charged surfaces: Current status and future perspectives. Frontiters in Material Science & Engineering workshop: Hydrogen Interaction in Metals, Max-Planck Institut für Eisenforschung, Düsseldorf, Germany (2017)
Brinckmann, S.; Fink, C.; Dehm, G.: Severe Microscale Deformation of Pearlite and Cementite. 2017 MRS Spring Meeting & Exhibits, Phoenix, AZ, USA (2017)
Luo, W.; Kirchlechner, C.; Dehm, G.; Stein, F.: Fracture Toughness of Hexagonal and Cubic NbCo2 Laves Phases. Nanobrücken 2017, European Nanomechanical Testing Conference, University of Manchester, Manchester, UK (2017)
Dehm, G.: Resolving the interplay of nanostructure and mechanical properties in advanced materials. Karlsruher Werkstoffkolloquium im Wintersemester 2016/2017, Karlsruhe, 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
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…