Neugebauer, J.: Solvent-controlled single atom dissolution, surface alloying and Wulff shapes of nanoclusters; Electrocatalysis at electrocodes in the dry. Workshop: Research Area III, ZEMOS, Bochum, Germany (2016)
Neugebauer, J.: Collective variable description of crystal anharmonicity. IPAM Workshop II: Collective Variables in Classical Mechanics, Los Angeles, CA, USA (2016)
Neugebauer, J.: Modelling structural materials in extreme environments by ab initio guided multiscale simulations. International Workshop “Theory and Modelling of Materials in Extreme Environment", Abingdon, UK (2016)
Neugebauer, J.: Ab initio thermodynamic description of advanced structural materials: Status and challenges. Workshop “Ab-initio Based Modeling of Advanced Materials”, Yekaterinburg, Russia (2016)
Neugebauer, J.: Stahl: Wie ein alter Werkstoff sich immer wieder neu erfindet und damit Wissenschaft und Wirtschaft beflügelt. 129. Versammlung der Gesellschaft der deutschen Naturforscher und Ärzte, Greifswald, Germany (2016)
Dutta, B.; Hickel, T.; Neugebauer, J.: Intermartensitic Phase Boundaries in Ni–Mn–Ga Alloys: A Viewpoint from Ab initio Thermodynamics. 5th International Conference on Ferromagnetic Shape Memory Alloys, Sendai, Japan (2016)
Zendegani, A.; Körmann, F.; Hickel, T.; Hallstedt, B.; Neugebauer, J.: Thermodynamic properties of the quaternary Q phase in Al–Cu–Mg–Si: a combined ab-initio, phonon and compound energy formalism approach. International Conference on Advanced Materials Modelling (ICAMM), Rennes, France (2016)
Neugebauer, J.: Ab initio description of defects in materials under extreme conditions. 2016 Joint ICTP-CAS-IAEA School and Workshop on Plasma-Material Interaction in Fusion Devices, Hefei, China (2016)
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
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.