Friák, M.; Sander, B.; Ma, D.; Raabe, D.; Neugebauer, J.: Theory-guided design of Ti-binaries for human implants. XVI. International Materials Research Congress, Cancun (Merrida), Mexico (2007)
Friák, M.; Sander, B.; Ma, D.; Raabe, D.; Neugebauer, J.: Ab initio prediction of elastic and thermodynamic properties of metals. Seminar in Friedrich-Alexander-Universitaet, Erlangen-Nürnberg, Germany (2007)
Friák, M.; Neugebauer, J.; Sander, B.; Raabe, D.: Theory-guided design of Ti-based binaries for human implants. Spring meeting of the German Physical Society (DPG), Regensburg, Germany (2007)
Raabe, D.; Sander, B.; Friák, M.; Neugebauer, J.: Bottom up design of novel Titanium-based biomaterials through the combination of ab-initio simulations and experimental methods. Materials Research Society fall meeting, Boston, MA, USA (2006)
Friak, M.; Sander, B.; Ma, D.; Raabe, D.; Neugebauer, J.: Theory-guided design of Ti–Nb alloys for biomedical applications. 1st International Conference on Material Modelling, Dortmund, Germany (2009)
Friák, M.; Ma, D.; Sander, B.; Raabe, D.; Neugebauer, J.: Bottom up design of novel titanium-based biomaterials through the combination of ab-initio simulations and experimental methods. Euromat 2007, Nürnberg, Germany (2007)
Friák, M.; Neugebauer, J.; Sander, B.; Raabe, D.: Ab initio study of chemical and structural trends of Ti-based binary alloys. Materials Research Society fall meeting, Boston, MA, USA (2006)
Sander, B.: Gefüge- und Eigenschaftsuntersuchungen an den Ti-20Mo-7Zr-5Ta und Ti-35Nb-7Zr-5Ta β-Titanlegierungen für medizinische Implantate. Bachelor, FH Düsseldorf, Düsseldorf (2008)
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
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…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.