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
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…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.