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)
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
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.