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)
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)
Ma, D.; Raabe, D.; Roters, F.: Effects of initial orientation, sample geometry and friction on anisotropy and crystallographic orientation changes in single crystal microcompression deformation: A crystal plasticity finite element study. International workshop on small scale plasticity, Brauwald, Switzerland (2007)
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
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.
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.