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
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.
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…
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…