Raabe, D.; Fabritius, H.; Nikolov, S.; Petrov, M.; Friak, M.; Elstnerová, P.; Neugebauer, J.: Ab initio based multiscale modeling of biological composites: Example of the exoskeleton of the lobster Homarus Americanus. Colloquium Lecture, Center for Nanoscience CeNS, Ludwigs-Maximilians Universität München, München, Germany (2010)
Elstnerová, P.; Friák, M.; Neugebauer, J.: Ab initio study of calcite substituted by Mg and P. Seminar talk at Masaryk University, Brno, Czech Republic (2009)
Elstnerová, P.; Friák, M.; Neugebauer, J.: Ab initio study of calcite substituted by Mg and P. Multiscale design modeling 2009, Brno, Czech Republic (2009)
Elstnerová, P.; Friák, M.; Neugebauer, J.: Crustacean skeletal elements: Variations in the constructional morphology at different hierarchical levels. Seminar talk at Masaryk University, Brno, Czech Republic (2009)
Elstnerová, P.; Friák, M.; Neugebauer, J.: Enhancing mechanical properties of calcite by Mg substitutions - A quantum-mechanical Study. 75. Annual Meeting of the DPG, Dresden, Germany (2011)
Elstnerová, P.; Friák, M.; Neugebauer, J.: Ab initio study of thermodynamic, structural, and elastic properties of Mg-substituted crystalline calcite. 4. Wiener Biomaterialsymposium, Vienna, Austria (2010)
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
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.
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…