Dehm, G.: Fracture testing of thin films: insights from synchrotron XRD and micro-cantilever experiments. 2016 MRS Fall Meeting, Boston, MA, USA (2016)
Dehm, G.; Harzer, T. P.; Dennenwaldt, T.; Freysoldt, C.; Liebscher, C.: Chemical demixing and thermal stability of supersaturated nanocrystalline CuCr alloys: Insights from advanced TEM. MS&T '16, Materials Science & Technology 2016 Conference & Exhibition, Salt Lake City, UT, USA (2016)
Dehm, G.: Resolving the interplay of nanostructure and mechanical properties by advanced electron microscopy. MSE Conference, Materials Science and Engineering, Darmstadt, Germany (2016)
Kirchlechner, C.; Malyar, N.; Dehm, G.: Insights into dislocation grain-boundary interaction by X-ray µLaue diffraction. Dislocations 2016, West Lafayette, IN, USA (2016)
Dehm, G.: Deformation and Adhesion of Metallic Thin Films. International Conference on Metallurgical Coatings and Thin Films, 43rd ICMCTF, San Diego, CA, USA (2016)
Kirchlechner, C.; Malyar, N.; Imrich, P. J.; Dehm, G.: Dislocation twin boundary interaction and its dependence on loading direction. 62. Metallkunde-Kolloquium, Lech am Arlberg, Austria (2016)
Harzer, T. P.; Duarte, M. J.; Dehm, G.: In-situ TEM isothermal annealing of nanocrystalline supersaturated Cu–Cr thin film alloys. 80th Annual Conference of the DPG and DPG Spring Meeting, Regensburg, Germany (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
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…