Cherevko, S.; Geiger, S.; Kasian, O.; Mingers, A. M.; Mayrhofer, K. J. J.: Oxygen evolution activity and stability of iridium in acidic media. Part 2. – Electrochemically Grown Hydrous Iridium Oxide. Journal of Electroanalytical Chemistry 774, pp. 102 - 110 (2016)
Ledendecker, M.; Mondschein, J. S.; Žeradjanin, A. R.; Cherevko, S.; Geiger, S.; Schalenbach, M.; Schaak, R. E.; Mayrhofer, K. J. J.: Stability of binary metallic ceramics in the HER reaction - feasible HER electrocatalysts in acidic medium? In Abstracts of Papers of the American Chemical Society, 254, 350. 254th National Meeting and Exposition of the American-Chemical-Society
(ACS) on Chemistry's Impact on the Global Economy, Washington, DC, August 20, 2017 - August 24, 2017. (2017)
Geiger, S.; Cherevko, S.; Mayrhofer, K. J. J.: Platinum dissolution in presence of chlorides. 3rd Ertl Symposium on Surface Analysis and Dynamics
, Berlin, Germany (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
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.