Neugebauer, J.: Ab initio based modeling of engineering materials: From a predictive thermodynamic description to tailored mechanical properties. Multiscale Materials Modeling 2008, Tallahassee, FL, USA (2008)
Udyansky, A.; Bugaev, V.; von Pezold, J.; Friák, M.; Neugebauer, J.: Modeling of the strain-induced interaction between carbon atoms in Fe-C solid solution using embedded atom method potential. Contemporary Problems of Metal Physics, Kiev, Ukraine (2008)
Neugebauer, J.: Design of engineering materials based on ab initio thermodynamics and kinetics. Materials Science and Technology 2008, Pittsburgh, PA, USA (2008)
Lymperakis, L.; Neugebauer, J.: Ab initio study of Thermodynamics and adatom kinetics on non-polar GaN surfaces: Consequences on the growth morphology and the formation of nanowires. International Workshop on Nitride Semiconductors, Montreux, Switzerland (2008)
Ma, A.; Friák, M.; Neugebauer, J.; Raabe, D.: Ab initio based design of alloys. MS&T'08, Symposium: Discovery and Optimization of Materials Through Computational Design, David Lawrence Convention Center, Pittsburgh, PA, USA (2008)
Counts, W. A.; Ma, D.; Friák, M.; Neugebauer, J.; Raabe, D.: Multiscale design of aluminium alloys based on ab-initio methods. ICAA 11 – 11th International Conference on Aluminium Alloys 2008, Aachen, Germany (2008)
Lymperakis, L.; Neugebauer, J.: Growth simulations of non-polar GaN surfaces: Thermodynamics, kinetics and dopant incorporations. Bremen DFG Forschergruppe: Workshop in Riezlern, Reizlern, Austria (2008)
Marquardt, O.; Hickel, T.; Neugebauer, J.: Modeling of electronic and optical properties of GaN/AlN quantum dots by using the k.p-method. Bremen DFG Forschergruppe: Workshop in Riezlern, Riezlern, Austria (2008)
Raabe, D.; Friak, M.; Neugebauer, J.; Counts, W. A.: Homogenization in Polycrystal Mechanics on the Basis of First Principles Simulations. IUTAM Symposium on Variational Concepts in Materials Mechanics, Ruhr-Universität Bochum, Germany (2008)
Dick, A.; Hickel, T.; Neugebauer, J.: First Principles Predictions of Mechanical Properties of FeMn-Alloys. Workshop des SFB761, Beilngries, Germany (2008)
Friák, M.; Neugebauer, J.: Ab initio study on elastic properties of Fe3Al-based alloys. Materials Science and Engineering (MSE'08), Nürnberg, Germany (2008)
Neugebauer, J.: Ab Initio Based Modeling of Engineering Materials: From a Predictive Thermodynamic Description to Tailored Mechanical Properties. Materials Science and Engineering, Nürnberg, Germany (2008)
von Pezold, J.; Neugebauer, J.: Hydrogen-enhanced local plasticity - An atomistic study. Materials Science and Engineering 2008, Nuernberg, Germany (2008)
Ismer, L.; Ireta, J.; Neugebauer, J.: First principles study of vibrational and thermodynamic properties of the secondary structure of proteins. Computational Materials Science Workshop, Ebernburg Castle, Germany (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.