Dey, P.; Nazarov, R.; Friák, M.; Hickel, T.; Neugebauer, J.: kappa-carbides as precipitates in austenitic steels: Ab initio study of structural, magnetic and Interface properties. EUROMAT 2013, Sevilla, Spain (2013)
Neugebauer, J.: Ab initio based multiscale modeling of structural materials: From a predictive thermodynamic description to tailored mechanical properties. CECAM Conference, Platja d’Aro, Spain (2013)
Dutta, B.; Körmann, F.; Dey, P.; Hickel, T.; Neugebauer, J.: Ab-initio based prediction of chemical trends for phase transitions in magnetic shape memory alloys. Weekly Seminar, Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-Universität Bochum, Bochum, Germany (2013)
Lymperakis, L.; Weidlich, P. H.; Eisele, H.; Schnedler, M.; Nys, J.-P.; Grandidier, B.; Stievenard, D.; Dunin-Borkowski, R.; Neugebauer, J.; Ebert, P.: Revealing Hidden Surface States of Non-Polar GaN Facets by an Ab Initio Tailored STM Approach. 10th International Conference on Nitride Semiconductors, Washigton DC, USA (2013)
Dutta, B.; Hickel, T.; Neugebauer, J.: Ab-initio based prediction of chemical trends in magnetic shape memory alloys. Mini Workshop on Lattice Dynamics, Uppsala University, Uppsala, Sweden (2013)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Modelling Mica from first-principles. 1st Dutch/German Workshop on Computational Materials Design, Balk, The Netherlands (2013)
Dutta, B.; Hickel, T.; Neugebauer, J.: Phase transitions in magnetic shape memory alloys: Ab-initio based prediction of chemical trends. Fourth International Conference on Ferromagnetic Shape Memory Alloys (ICFSMA'13), Boise, ID, USA (2013)
Ilhan, M.; Todorova, M.; Neugebauer, J.: Adsorption of H, S, and O on the Iron (100) surface. 1st Dutch/German Workshop on Computational Materials Design, Balk, The Netherlands (2013)
Izanlou, A.; Todorova, M.; Neugebauer, J.: Interactions of water and its derivatives with low index Fe3Al surfaces. 1st Dutch/German Workshop on Computational Materials Design, Balk, The Netherlands (2013)
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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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.
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…