Peranio, N.; Schulz, S.; Li, Y. J.; Roters, F.; Raabe, D.; Masimov, M.; Springub, G.: Processing of dual-phase steel for automotive applications: Microstructure and texture evolution during annealing and numerical simulation by cellular automata. Euromat 2009 (European Congress and Exhibition on Advanced Materials and Processes), Glasgow, UK (2009)
Butz, A.; Rist, T.; Springub, B.; Roters, F.; Schulz, S.; Peranio, N.; Lossau, S.: From Cold Rolling to Deep Drawing - Microstructure Based Modeling of a Dual Phase Steel. NUMISHEET 2008, Interlaken, Switzerland (2008)
Springub, G.; Masimov, M.; Peranio, N.; Li, Y. J.; Roters, F.; Raabe, D.: Study of substructure and texture development in dual phase steels due to thermo-mechanical treatment. ITAP3, 3d International Conference on Texture and Anisotropy in Polycrystals, Göttingen, 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…