Raabe, D.: Compositional Lattice Defect Manipulation for Microstructure Design. The Bauerman Lecture 2019, Department of Materials, Imperial College London, Royal School of Mines, London, UK (2019)
Sedighiani, K.; Diehl, M.; Roters, F.; Sietsma, J.; Raabe, D.: Obtaining constitutive parameters for a physics-based crystal plasticity model from macro-scale behavior. International Conference on Plasticity, Damage, and Fracture , Panama City, Panama (2019)
Li, Z.; Su, J.; Lu, W.; Wang, Z.; Raabe, D.: Metastable high-entropy alloys: design, structure and properties. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
Seol, J. B.; Ko, W.-S.; Bae, J. W.; Jo, Y. H.; Li, Z.; Choi, P.-P.; Raabe, D.; Kim, H. S.: Transition in boron boundary cohesion from effectiveness to harmfulness with respect to application temperatures: high-entropy alloys and Ni-based superalloys. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
Lu, W.; Li, Z.; Liebscher, C.; Dehm, G.; Raabe, D.: TEM/STEM Investigations of the TRIP Effect in a Dual-Phase High-Entropy Alloy. MRS Fall Meeting, Boston, MA, USA (2018)
Su, J.; Li, Z.; Raabe, D.: Microstructural Design to Improve the Mechanical Properties of an Interstitial TRIP-TWIP High-Entropy Alloy. MRS Fall Meeting , Boston, MA, USA (2018)
Sun, B.; Ponge, D.; Fazeli, F.; Scott, C.; Yue, S.; Raabe, D.: Revealing fracture mechanisms of medium manganese steels with and without delta-ferrite. 6th International Conference on Advanced Steels (ICAS 2018), Jeju, South Korea (2018)
Diehl, M.; Kühbach, M.; Raabe, D.: Experimental–computational analysis of primary static recrystallizazion in DC04 steel. 9th International Conference on Multiscale Materials Modeling , Osaka, Japan (2018)
Diehl, M.; Shanthraj, P.; Eisenlohr, P.; Roters, F.; Raabe, D.: DAMASK - Düsseldorf Advanced Material Simulation Kit. Seminar of the Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA (2018)
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…
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.