Raabe, D.: Atomic-Scale Analysis of Chemistry at Lattice Defects. The KAIST Lecture in Materials Science and Engineering 2019, Korea Advanced Institute of Science and Technology KAIST, Daejeon, Korea (2019)
Su, J.; Raabe, D.; Li, Z.: On the mechanism of displacive phase transformation in metastable high entropy alloys. DPG Regensburg 2019, Regensburg, Germany (2019)
Raabe, D.: Compositional Lattice Defect Manipulation for Microstructure Design. The Bauerman Lecture 2019, Department of Materials, Imperial College London, Royal School of Mines, London, UK (2019)
Sedighiani, K.; Diehl, M.; Roters, F.; Sietsma, J.; Raabe, D.: Obtaining constitutive parameters for a physics-based crystal plasticity model from macro-scale behavior. International Conference on Plasticity, Damage, and Fracture , Panama City, Panama (2019)
Li, Z.; Su, J.; Lu, W.; Wang, Z.; Raabe, D.: Metastable high-entropy alloys: design, structure and properties. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
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…