Nikolov, S.; Sachs, C.; Fabritius, H.; Raabe, D.; Petrov, M.; Friák, M.; Neugebauer, J.: Modeling of the mechanical properties of lobster cuticle from ab initio to macroscale: How nature designs multifunctional composites with optimal properties. International Plasticity Conference 2009, Virgin Islands, USA (2009)
Ohsaki, S.; Raabe, D.; Hono, K.: Mechanical alloying and amorphization in Cu–Nb–Ag in situ composite wires studied by TEM and atom probe tomography. MRS 2009 Fall Meeting, Boston, MA, USA (2009)
Raabe, D.; Demir, E.; Zaefferer, S.: Experimental investigation of geometrically necessary dislocations beneath small indents of different depths using EBSD tomography. MRS 2009 Fall Meeting, Boston, MA, USA (2009)
Hild, S.; Ziegler, A.; Neues, F.; Epple, M.; Fabritius, H.; Raabe, D.: The Crustacean Cuticle: A Model to Study the Influence of Chemical Composition and Microstructure on the Mechanical Properties of a Biological Composite Material. MRS Fall Conference 2008, Boston, MA, USA (2008)
Zambaldi, C.; Roters, F.; Zaefferer, S.; Raabe, D.: Ductility of Gamma-TiAl-Based Microstructures in the Light of Deformation Mode Interaction-Crystal Plasticity Modeling and Micro-Mechanical Experiments. MRS Fall Conference 2008, Boston, MA, USA (2008)
Counts, W. A.; Friák, M.; Battaile, C.; Raabe, D.; Neugebauer, J.: Multiscale Prediction of Polycrystal Elastic Properties of Ultralight Weight Mg-Li Alloys using Ab Initio and FEM Approaches. MRS Fall Conference 2008, Boston, MA, USA (2008)
Demir, E.; Raabe, D.; Zaefferer, S.: Quantification of Geometrically Necessary Dislocations Beneath Small Indents of Different Depths Using EBSD Tomography. MRS Fall Conference 2008, Boston, MA, USA (2008)
Knezevic, M.; Ma, D.; Raabe, D.; Kalidindi, S. R.; Friák, M.; Neugebauer, J.: Application of Spectral Methods for Anisotropy Design of Ti-Nb Polycrystals for Biomedical Applications based on ab Initio Elastic Single Crystal Constants and Fast Fourier Homogenization. MRS Fall Conference 2008, Boston, MA, USA (2008)
Petrov, M.; Friák, M.; Lymperakis, L.; Neugebauer, J.; Raabe, D.: Ground-state structure and elastic anisotropy of crystalline alpha-chitin: An ab-initio based conformational analysis. Materials Research Society meeting (MRS), Boston, MA, USA (2008)
Calcagnotto, M.; Ponge, D.; Raabe, D.: Mechanical properties of ultrafine and fine grained dual phase steels. MS&T 2008 (Materials Science and Technology), Pittsburgh, PA, USA (2008)
Ma, A.; Friák, M.; Neugebauer, J.; Raabe, D.: Ab initio based design of alloys. MS&T'08, Symposium: Discovery and Optimization of Materials Through Computational Design, David Lawrence Convention Center, Pittsburgh, PA, USA (2008)
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…