Calderón, L. A. Á.; Shakeel, Y.; Gedsun, A.; Forti, M.; Hunke, S.; Han, Y.; Hammerschmidt, T.; Aversa, R.; Olbricht, J.; Chmielowski, M.et al.; Stotzka, R.; Bitzek, E.; Hickel, T.; Skrotzki, B.: Management of reference data in materials science and engineering exemplified for creep data of a singlecrystalline Nibased superalloy. Acta Materialia 286, 120735 (2025)
Atila, A.; Bitzek, E.: Atomistic origins of deformation-induced structural anisotropy in metaphosphate glasses and its influence on mechanical properties. Journal of Non-Crystalline Solids 627, 122822 (2024)
Webler, R.; Baranova, P. N.; Karewar, S.; Möller, J. J.; Neumeier, S.; Göken, M.; Bitzek, E.: On the influence of Al-concentration on the fracture toughness of NiAl: Microcantilever fracture tests and atomistic simulations. Acta Materialia 234, 117996 (2022)
Hiremath, P.; Melin, S.; Bitzek, E.; Olsson, P. A. T.: Effects of interatomic potential on fracture behaviour in single- and bicrystalline tungsten. Computational Materials Science 207 (18), 111283 (2022)
Gabel, S.; Merle, B.; Bitzek, E.; Göken, M.: A new method for microscale cyclic crack growth characterization from notched microcantilevers and application to single crystalline tungsten and a metallic glass. Journal of Materials Research 37, pp. 2061 - 2072 (2022)
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…