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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.