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
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…