Race, C. P.; von Pezold, J.; Neugebauer, J.: Grain boundary migration via the nucleation and growth of islands in molecular dynamics. 1st Austrian-German Workshop on Computational Materials Design, Kramsach, Austria (2012)
Elstnerová, P.; Friák, M.; Šob, M.; Neugebauer, J.: Prediction of the Ground State of NiN and Ni2N within the Quantum Mechanical Study. Multiscale Design of Advanced Materials, Brno, Czech Republic (2011)
Hickel, T.; Glensk, A.; Grabowski, B.; Neugebauer, J.: Ab initio up to the melting point: Integrated approach to derive accurate thermodynamic data for Al alloys. European Aluminium Association, European Aluminium Technology Platform, Working Group 5: Predictive Modelling, 5th workshop: ab initio modelling, Aachen, Germany (2011)
Hickel, T.; Al-Zubi, A.; Neugebauer, J.: Ab initio based prediction of phase diagrams: Application to magnetic shape-memory alloys. 9. Materialwissenschaftlicher Tag der Ruhr-Universtät Bochum, Bochum, Germany (2011)
Neugebauer, J.: Fully ab initio determination of free energies: Methodological challenges and applications. Conference on Computational Physics (CCP2011), Gatlinburg, TN, USA (2011)
Freysoldt, C.; Pfanner, G.; Neugebauer, J.: The dangling-bond defect in amorphous silicon: Insights from theoretical calculations of the EPR parameters. Workshop on Advanced EPR for material and solar energy research, Berlin, Germany (2011)
Izanlou, A.; Todorova, M.; Friák, M.; Palm, M.; Neugebauer, J.: Theoretical study of the environmental effect of H-containing gases on Fe–Al surfaces. FeAl2011, Discussion Meeting on the Development of Innovative Iron Aluminium Alloys, Lanzarote, Canary Islands, Spain (2011)
Neugebauer, J.: Doping and growth issues in group-III nitrides: An ab initio perspective. Workshop on III-Nitrides Growth, Characterization and Simulation, Berlin, Germany (2011)
Neugebauer, J.: Ab initio guided materials characterization and design. Science Vision for the European Spallation Source, Bad Reichenhall, Germany (2011)
Elstnerová, P.; Friák, M.; Neugebauer, J.: Enhancing mechanical properties of calcite by Mg substitutions - A Quantum-Mechanical Study. 12th International Symposium on Physics of Materials, Prague, Czech Republic (2011)
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.