Kim, J.-K.; Guo, W.; Choi, P.-P.; Raabe, D.: Compositional evolution of long-period stacking ordered structures in magnesium studied by atom probe tomography. Scripta Materialia 156, pp. 55 - 59 (2018)
Luo, Y.; Wang, X.; Guo, W.; Rohwerder, M.: Growth behavior of initial product layer formed on Mg alloy surface induced by polyaniline. Journal of the Electrochemical Society 162 (6), pp. C294 - C301 (2015)
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…