Lymperakis, L.; Neugebauer, J.: Exploring the 5D configurational space of grain boundaries in aluminun: An ab-initio based multiscale analysis. MRS Fall Meeting, Boston, MA, USA (2006)
Wahn, M.; Neugebauer, J.: Generalized Wannier Functions: An efficient way to construct ab-initio tight-binding orbitals for group-III nitrides. 6th International Conference on Nitride Semiconductors, Bremen, Germany (2005)
Hickel, T.; Grabowski, B.; Neumann, K.; Neumann, K.-U.; Ziebeck, K. R. A.; Neugebauer, J.: Temperature dependent properties of Ni-rich Ni2MnGa. Materials Research Society fall meeting, Boston, MA, USA (2005)
Ismer, L.; Ireta, J.; Neugebauer, J.: Thermodynamic stability of the secondary structure of proteins: A DFT-GGA based vibrational analysis. IPAM-Workshop: Multiscale Modeling in Soft Matter and Bio-Physics, Los Angeles, CA, USA (2005)
Lymperakis, L.; Neugebauer, J.: Ab-initio based multiscale calculations of low-angle grain boundaries in Aluminium. Materials Research Society fall meeting, Boston, MA, USA (2005)
Neugebauer, J.: Application and Implementation of Electronic Structure Methods. Lecture: Ruhr-Universität Bochum, SS 2015, Bochum, Germany, April 01, 2015 - September 30, 2015
Neugebauer, J.: Application and Implementation of Electronic Structure Methods. Lecture: Ruhr-Universität Bochum, SS 2014, Bochum, Germany, April 01, 2014 - September 30, 2014
Neugebauer, J.: Application and Implementation of Electronic Structure Methods. Lecture: Ruhr-Universität Bochum, SS 2013 , Bochum, Germany, April 01, 2013 - September 30, 2013
Neugebauer, J.; Hickel, T.: Moderne Computersimulations-Methoden in der Festkörperphysik. Lecture: Hands-on-Tutorial, Ruhr-Universität Bochum, Bochum, Germany, September 20, 2010 - September 24, 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
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.