Wu, X.; Erbe, A.; Fabritius, H. O.; Raabe, D.: Ultrastructural Origins of Optical Properties in the Exoskeletons of Beetles. 2011 MRS Fall Meeting, Boston, MA, USA (2011)
Wu, X.; Erbe, A.; Fabritius, H.; Raabe, D.: Structure of the 3D-Photonic Crystals in the Multi-Colored Scales of the Weevil Entimus imperialis (Curculionidae). Ninth International Conference on Photonic and Electromagnetic Crystal Structures (PECS-IX 2010), Granada, Spain (2010)
Wu, X.; Erbe, A.; Fabritius, H.; Raabe, D.: Spectral and angular distribution of light scattered from the elytra of two carabid beetle species. First NanoCharm Workshop on Advanced Polarimetric Instrumentation, Ecole Polytechnique, Palaiseau Cedex, Palaiseau Cedex, France (2009)
Wu, X.: Structure-property-relations of cuticular photonic crystals evolved by different beetle groups (Insecta, Coleoptera). Dissertation, RWTH-Aachen, Aachen, Germany (2014)
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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
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.