Zhang, J.; Tasan, C. C.; Lai, M.; Springer, H.; Raabe, D.: Microstructural and Mechanical Characterization of Cold Work Effects in GUM Metal. 9th International Conference on Advances in Experimental Mechanics, Cardiff, UK (2013)
Springer, H.; Kostka, A.: Verbinden von hochfestem Stahl mit einer Aluminiumlegierung durch Rührreibschweißen. 4. GKSS Workshop, Geesthacht, Germany (2009)
Belde, M. M.; Springer, H.; Inden, G.; Raabe, D.: Tailoring multi-phase steel microstructures by controlling local chemical gradients. MSE 2014, Darmstadt, Germany (2014)
Lai, M.; Tasan, C. C.; Zhang, J.; Grabowski, B.; Huang, L.; Springer, H.; Raabe, D.: ω phase accommodated nano-twinning mechanism in Gum Metal: An ab initio study. 3rd International Workshop on Physics Based Material Models and Experimental Observations: Plasticity and Creep, Cesme/Izmir, Turkey (2014)
Springer, H.: A novel roll bonding methodology for the cross-scale analysis of phase properties and interac-tions in multiphase structural materials. MSE 2014, Darmstadt, Germany (2014)
Springer, H.; Kostka, A.: Verbinden von hochfestem Stahl mit einer Aluminiumlegierung durch Rührreibschweißen. 4. GKSS Workshop, Geesthacht, Germany (2009)
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…