Raabe, D.; Mattissen, D.: Experimental investigation and Ginzburg-Landau modeling of the microstructure dependence of superconductivity in Cu–Ag–Nb wires. Acta Materialia 47 (3), pp. 769 - 777 (1999)
Mattissen, D.; Raabe, D.; Heringhaus, F.: Experimental investigation and modeling of the influence of microstructure on the resistive conductivity of a Cu–Ag–Nb in situ composite. Acta Materialia 47, pp. 1627 - 1634 (1999)
Marx, V.; Raabe, D.; Engler, O.; Gottstein, G.: Simulation of the texture evolution during annealing of cold rolled BCC and FCC matals using a cellular automation approach. Textures and Microstructures 28, pp. 211 - 218 (1997)
Raabe, D.: Texture simulation for hot rolling of aluminium by use of a Taylor model considering grain interactions. Acta Metallurgica et Materialia 43 (3), pp. 1023 - 1028 (1995)
Roters, F.; Eisenlohr, P.; Bieler, T. R.; Raabe, D.: Crystal Plasticity Finite Element Methods in Materials Science and Engineering. Wiley-VCH, Weinheim (2010), 197 pp.
Janssens, K. G. F.; Raabe, D.; Kozeschnik, E.; Miodownik, M. A.; Nestler, B.: Computational Materials Engineering – An Introduction to Microstructure Evolution. Academic Press, Elsevier, USA (2007), 360 pp.
Shanthraj, P.; Diehl, M.; Eisenlohr, P.; Roters, F.; Raabe, D.: Spectral Solvers for Crystal Plasticity and Multi-physics Simulations. In: Handbook of Mechanics of Materials, pp. 1347 - 1372 (Eds. Hsueh, C.-H.; Schmauder, S.; Chen, C.-S.; Chawla, K. K.; Chawla, N. et al.). Springer, Singapore (2019)
Friák, M.; Raabe, D.; Neugebauer, J.: Ab Initio Guided Design of Materials. In: Structural Materials and Processes in Transportation, pp. 481 - 495 (Eds. Lehmhus, D.; Busse, M.; Herrmann, A. S.; Kayvantash, K.). Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany (2013)
Tikhovskiy, I.; Raabe, D.; Roters, F.: Anwendung der Textur-Komponenten-Kristallplastizitäts-FEM für die Simulation von Umformprozessen unter Berücksichtigung des Texturgradienten. In: Prozessskalierung, Strahltechnik, Tagungsband des 2. Kolloquiums Prozessskalierung im Rahmen des DFG Schwerpunktprogramms Prozessskalierung, Vol. 27, pp. 157 - 166 (Ed. Vollertsen, F.). BIAS-Verlag, Bremen (2005)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.
The project HyWay aims to promote the design of advanced materials that maintain outstanding mechanical properties while mitigating the impact of hydrogen by developing flexible, efficient tools for multiscale material modelling and characterization. These efficient material assessment suites integrate data-driven approaches, advanced…
A novel design with independent tip and sample heating is developed to characterize materials at high temperatures. This design is realized by modifying a displacement controlled room temperature micro straining rig with addition of two miniature hot stages.
The structures of grain boundaries (GBs) have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the near-atomic scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability…
While Density Functional Theory (DFT) is in principle exact, the exchange functional remains unknown, which limits the accuracy of DFT simulation. Still, in addition to the accuracy of the exchange functional, the quality of material properties calculated with DFT is also restricted by the choice of finite bases sets.