Grabowski, B.; Söderlind, P.; Hickel, T.; Neugebauer, J.: Ab Initio Thermodynamics of the fcc-bcc Transition in Ca Including All Relevant FiniteTemperature Excitation Mechanisms. TMS 2012, Orlando, FL, USA (2012)
Grabowski, B.: Ab initio prediction of materials properties up to the melting point. Condensed Matter and Materials Division seminar series, Lawrence Livermore National Lab, Livermore, CA, USA (2012)
Grabowski, B.: Ab initio prediction of materials properties up to the melting point. Seminar: "Ab initio Description of Iron and Steel: Thermodynamics and Kinetics", Tegernsee, Germany (2012)
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)
Grabowski, B.; Hickel, T.; Glensk, A.; Neugebauer, J.: Integrated approach to derive thermodynamic data for pure Al and Al alloys up to the melting point. Psi-k Conference 2010, Berlin, Germany (2010)
Glensk, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Ab initio prediction of thermodynamic data for selected phases of the Al-Mg-Si-Cu system. CECAM Summer School on Computational Materials Sciences, San Sebastian, Spain (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.