Grabowski, B.: Modern materials design from first-principles: Recent progress and future prospects. Seminar, Imperial College London, London, UK (2015)
Grabowski, B.: Ab initio thermodynamics of the CoCrFeMnNi high entropy alloy: Importance of entropy contributions beyond the configurational one. ICAMS Seminar, Ruhr-University Bochum, Bochum, Germany (2015)
Grabowski, B.: Random phase approximation up to the melting point: The impact of anharmonicity and non-local many-body effects on the thermodynamics of Au. MISIS Workshop, Moscow, Russia (2015)
Körmann, F.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Temperature-dependent coupling of atomic and magnetic degree of freedom from first-principles. Electronic Structure Theory for the Accelerated Design of Structural Materials, Moscow, Russia (2015)
Grabowski, B.; Wippermann, S. M.; Glensk, A.; Hickel, T.; Neugebauer, J.: Random phase approximation up to the melting point: Impact of anharmonicity and nonlocal many-body effects on the thermodynamics of Au. DPG Spring Meeting 2015, Berlin, Germany (2015)
Hickel, T.; Glensk, A.; Grabowski, B.; Körmann, F.; Neugebauer, J.: Thermodynamics of materials up to the melting point: The role of anharmonicities. Asia Sweden Meeting on Understanding Functional Materials from Lattice dynamics, Guwahati, India (2014)
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
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…