Zaefferer, S.: An overview on techniques for high spatial resolution measurements of plastic and elastic strain by EBSD and related techniques. RexGG pre-conference workshop, Wollongong, Australia (2013)
Zaefferer, S.; Konijnenberg, P. J.: Advanced analysis of 3D EBSD data obtained from FIB-EBSD tomography. RexGG pre-conference workshop, Wollongong, Australia (2013)
Zaefferer, S.: An overview on techniques for high spatial resolution measurements of plastic and elastic strain by EBSD and related techniques. MicroCar 2013, Leipzig, Germany (2013)
Schemmann, L.; Zaefferer, S.: First experiences using a low-energy WDX spectrometer (LEXS) on a FEG-SEM for carbon determination on a martensitic steel. EMAS 2013, Porto, Portugal (2013)
Schemmann, L.; Zaefferer, S.; Raabe, D.: Influence of the inheritance of chemical elements on the transformation behaviour during intercritical annealing of DP steel strips. Euromat 2013, Sevilla, Spain (2013)
Zaefferer, S.: Techniques and application of 3D orientation microscopy based on EBSD tomography. GN-MEBA (groupement nationale de microscopie electronique a balayage) 2013, Paris, France (2013)
Zaefferer, S.: Combined Application of EBSD and ECCI for Crystal Defect Observation in Bulk Samples. GN-MEBA (groupement nationale de microscopie electronique a balayage) 2013, Paris, France (2013)
Zaefferer, S.; Elhami, N. N.: Theory and application of electron channelling contrast imaging (ECCI) of defects in metals. RMS EBSD 2013, Oxford, 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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…