Lübke, A.; Loza, K.; Patnaik, R.; Enax, J.; Raabe, D.; Prymak, O.; Fabritius, H.-O.; Gaengler, P.; Epple, M.: Reply to the ‘Comments on “Dental lessons from past to present: ultrastructure and composition of teeth from plesiosaurs, dinosaurs, extinct and recent sharks”’ by H. Botella et al., RSC Adv., 2016, 6, 74384–74388. RSC Advances 7 (11), pp. 6215 - 6222 (2017)
Prymak, O.; Stein, F.: The Ternary Cr–Al–Nb Phase Diagram: Experimental Investigations of Isothermal Sections at 1150, 1300 and 1450 °C. Journal of Alloys and Compounds 513, pp. 378 - 386 (2012)
Prymak, O.; Stein, F.: Solidification and High-Temperature Phase Equilibria in the Fe–Al-rich Part of the Fe–Al–Nb System. Intermetallics 18 (7), pp. 1322 - 1326 (2010)
Prymak, O.; Stein, F.; Kerkau, A.; Ormeci, A.; Kreiner, G.; Frommeyer, G.; Raabe, D.: Phase equilibria in the ternary Nb–Cr–Al system and site occupation in the hexagonal C14 Laves phase Nb(AlxCr1–x)2. In: Materials Research Society Symposium Proceedings, pp. 499 - 504 (Ed. Proceedings, M. S.). Materials Research Society Symposium. (2009)
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
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.