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)
Raabe, D.: Drowning in data - A viewpoint on strategies for doing science with simulations. In: Handbook of Materials Modeling, pp. 2687 - 2693 (Ed. Yip, S.). Springer, The Netherlands (2005)
Raabe, D.: Recrystallization Simulation by use of Cellular Automata. In: Handbook of Materials Modeling, pp. 2173 - 2203 (Ed. Yip, S.). Springer, Netherlands (2005)
Raabe, D.; Roters, F.: How do 10^10 crystals co-deform. In: Weitab vom Hookeschen Gesetz -- Moderne Ansätze der Ingenieurpraxis großer inelastischer Deformationen metallischer Werkstoffe (Eds. Kollmann, F. G.; G., G.; Akademie der Wissenschaften und der Literatur, Mainz, Germany). Franz Steiner Verlag, Stuttgart, Germany (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
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…