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Zaefferer, S.; Sato, H.: Investigation of the formation mechanism of martensite plates in Fe-30%Ni by a high resolution orientation microscopy in SEM. ESOMAT 2006, Bochum (2006)
Sato, H.; Zaefferer, S.: A study on the crystal orientation relationship of butterfly martensite in an Fe30 % Ni alloy by 3-D EBSD-based orientation microscopy. Microscopy Conference 2005, Davos, Switzerland (2005)
Sato, H.; Zaefferer, S.: 3D-analysis of the crystal orientation relationship and growth process of lenticular martensite in Fe–30mass%Ni alloy. DPG Frühjahrstagung, Berlin, Germany (2005)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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…