Roters, F.; Ma, A.; Raabe, D.: The Texture Component Crystal Plasticity Finite Element Method. Keynote lecture at the Third GAMM (Society for Mathematics and Mechanics) Seminar on Microstructures, Stuttgart, Germany (2004)
Raabe, D.: Metallkundliche Ursachen und mechanische Auswirkungen unvollständiger Rekristallisation. Werkstoffausschuß des Vereins Deutscher Eisenhüttenleute, VDEh, Düsseldorf, German (2004)
Raabe, D.: Polycrystal Mechanics of Metals and Polymers - Experiments and Theory. Colloquium Lecture at the Massachusetts Institute of Technology, Cambridge, USA (2003)
Wang, Y.; Roters, F.; Raabe, D.: Simulation of Texture and Anisotropy during Metal Forming with Respect to Scaling Aspects. 1st Colloquium Process Scaling, Bremen, Germany (2003)
Raabe, D.: Simulation of Texture and Anisotropy during Metal Forming with Respect to Scaling Aspects. Lecture at the 1st Colloquium on Process Scaling, Bremen (2003)
Raabe, D.: Experiments and Theory of Surface- and Polycrystal Mechanics. Colloquium Lecture at the Technical University of Hamburg-Harburg, Hamburg-Harburg (2003)
Kobayashi, S.; Zaefferer, S.; Schneider, A.; Raabe, D.; Frommeyer, G.: Slip system determination by rolling texture measurements around the strength peak temperature in a Fe3Al-based alloy. Intern. Conf. on Strength of Materials (ICSMA 13), Budapest, Hungary (2003)
Raabe, D.: Experimental and Theoretical Investigation of Grain Scale Plasticity. Colloquium lecture at the Department of Materials Science and Engineering of Northwestern University, Evanston, Chicago, USA (2002)
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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…