Hickel, T.; Al-Zubi, A.; Uijttewaal, M.; Neugebauer, J.: First principles determination of phase transitions in magnetic shape memory alloys. Multiscale Materials Modelling, Freiburg, Germany (2010)
Nazarov, R.; Hickel, T.; Neugebauer, J.: Interaction of H with vacancies in iron and steels: The combination of atomistic, thermodynamic and elastic effects. MMM 2010 Conference, Freiburg, Germany (2010)
Körmann, F.; Dick, A.; Hickel, T.; Neugebauer, J.: Integrating finite temperature magnetism into ab initio free energy calculations. ICAMS Scientific Retreat, Akademie Biggesee, Attendorn, Germany (2010)
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)
Friák, M.; Zhu, L.-F.; Dick, A.; Hickel, T.; Neugebauer, J.: First-principles study of the Ti-Fe eutectic system. Seminar at Institute of Physics of Materials at Czech Academy of Sciences, Brno, Czech Republic (2010)
Dick, A.; Hickel, T.; Neugebauer, J.: Ab Initio Interfacial Austenite/Martensite Energies for Accurate Deformation Mechanism Maps in High-Mn Steels. Materials Science and Engineering 2010, Darmstadt, Germany (2010)
Hickel, T.; Körmann, F.; Dick, A.; Neugebauer, J.: The thermodynamics of Fe-based compounds derived from first principles. Materials Science and Engineering 2010, Darmstadt, Germany (2010)
Nazarov, R.; Hickel, T.; Neugebauer, J.: Ab initio study on the cross-interaction between magnetism and point defects in fcc Fe. Realistic Theories of Correlated Electrons in Condensed Matter, Volga-River, Moscow, Russia (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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.