Wang, M.; Tasan, C. C.; Ponge, D.; Kostka, A.; Raabe, D.: Deformation micro-mechanisms in medium-Mn TRIP-maraging steel. 2nd International Conference on High Manganese Steel, HMnS 2014, Aachen, Germany (2014)
Tasan, C. C.; Springer, H.; Lai, M.; Zhang, J.-I.; Raabe, D.: Influence of oxygen on the deformation behavior of Ti–Nb–Ta–Zr alloys. Thermec 2013, Las Vegas, NV, USA (2013)
Tasan, C. C.; Diehl, M.; Yan, D.; Shanthraj, P.; Roters, F.; Eisenlohr, P.; Raabe, D.: Integrated in-situ experiments – full field crystal plasticity simulations to analyze stress – strain partitioning in multi-phase alloys. Nanomechanical Testing in Materials Research and Development IV, Olhão, Algarve, Portugal (2013)
Tasan, C. C.; Wang, M.; Ponge, D.; Kostka, A.; Raabe, D.: Size effects on austenite stability investigated by in-situ EBSD. BSSM 9th Int. Conf. on Advances in Experimental Mechanics, Cardiff, UK (2013)
Yan, D.; Tasan, C. C.; Raabe, D.: High resolution strain mapping coupled with EBSD during in-situ tension in SEM. BSSM 9th Int. Conf. on Advances in Experimental Mechanics, Cardiff, UK (2013)
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.
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.