Winning, M.: 3D EBSD measurements in ultra fine grained Cu 0.17wt% Zr obtained from ECAP. Seminar talk, Carnegie Mellon University, Pittsburgh, PA, USA (2008)
Khorashadizadeh, A.; Raabe, D.; Winning, M.: Three-dimensional tomographic EBSD measurements of the crystal topology in heavily deformed ultra fine grained pure Cu and Cu–0.17wt%Zr obtained from ECAP and HPT. DPG Frühjahrstagung 2008, Berlin, Germany (2008)
Winning, M.: Grain boundary engineering by application of mechanical stresses. The Third International Conference on Recrystallization and Grain Growth, Jeju Island, South Korea (2007)
Winning, M.; Raabe, D.; Brahme, A.: A texture component model for predicting recrystallization textures. The Third International Conference on Recrystallization and Grain Growth, Jeju Island, South Korea (2007)
Winning, M.: Korngrenzen auf Wanderschaft: Wege zum Design metallischer Werkstoffe. Colloquia Academia, Akademie der Wissenschaften und der Literatur, Mainz, Germany (2007)
Winning, M.: Korngrenzen auf Wanderschaft: Wege zum Design metallischer Werkstoffe. Colloquia Academia, Akademie der Wissenschaften und der Literatur, Mainz, Germany (2006)
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…