Sandim, M. J. R.; Tytko, D.; Kostka, A.; Choi, P.; Awaji, S.; Watanabe, K.; Raabe, D.: Grain boundary segregation in a bronze-route Nb3Sn superconducting wire studied by atom probe tomography. Superconductor Science and Technology 26, pp. 055008-1 - 055008-7 (2013)
Siboni, N. H.; Raabe, D.; Varnik, F.: Maintaining the equipartition theorem in small heterogeneous molecular dynamics ensembles. Physical Review E 87 (3), pp. 030101-1 - 030101-4 (2013)
Nematollahi, A.; von Pezold, J.; Neugebauer, J.; Raabe, D.: Thermodynamics of carbon solubility in ferrite and vacancy formation in cementite in strained pearlite. Acta Materialia 61 (5), pp. 1773 - 1784 (2013)
Seol, J.-B.; Raabe, D.; Choi, P.; Park, H. S.; Kwak, J. H.; Park, C. G.: Direct evidence for the formation of ordered carbides in a ferrite based low-density Fe–Mn–Al–C alloy studied by transmission electron microscopy and atom probe tomography. Scripta Materialia 68 (6), pp. 348 - 353 (2013)
Titrian, H.; Aydin, U.; Friák, M.; Ma, D.; Raabe, D.; Neugebauer, J.: Self-consistent scale-bridging approach to compute the elasticity of multi-phase polycrystalline materials. Materials Research Society Symposia Proceedings 1524, pp. 17 - 23 (2013)
Gutiérrez-Urrutia, I.; Raabe, D.: Influence of Al content and precipitation state on the mechanical behavior of austenitic high-Mn low-density steels. Scripta Materialia 68 (6), pp. 343 - 347 (2013)
Pinto de Siqueira, R.; Sandim, H. R. Z.; Raabe, D.: Particle Stimulated Nucleation in Coarse-Grained Ferritic Stainless Steel. Metallurgical and Materials Transactions A 44 (1), pp. 469 - 478 (2013)
Woldemedhin, M. T.; Raabe, D.; Hassel, A. W.: Characterization of thin anodic oxides of Ti–Nb alloys by electrochemical impedance spectroscopy. Electrochimica Acta 82, pp. 324 - 332 (2012)
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
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…