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)
Lenz, M.; Wu, M.; He, J.; Makineni, S. K.; Gault, B.; Raabe, D.; Neumeier, S.; Spiecker, E.: Atomic Structure and Chemical Composition of Planar Fault Structures in Co-Base Superalloys. 14th International Symposium on Superalloys, Superalloys 2021, Seven Springs, PA, USA, September 12, 2021 - September 16, 2021. Minerals, Metals and Materials Series, pp. 920 - 928 (2020)
Zhao, H.; Gault, B.; De Geuser, F.; Ponge, D.; Raabe, D.: Grain boundary segregation and precipitation in an Al–Zn–Mg–Cu alloy. In: edp Sciences, MATEC Web of Conferences, Section Plenary Lecture & ECR Award Recipients, Vol. 326, 01004. The 17th International Conference on Aluminium Alloys 2020 (ICAA17) , Grenoble, France, October 26, 2020 - October 29, 2020. (2020)
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
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.
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…
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…
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…