Leineweber, A.; Stein, F.: Comment on Hajra et al.: “High-temperature phase stability and phase transformations of Niobium-Chromium Laves phase: Experimental and first-principles calculation”. Materials and Design 247, 113373 (2024)
Flores, A.; Chatain, S.; Fossati, P.; Stein, F.; Joubert, J.-M.: Correction: Experimental Investigation and Thermodynamic Assessment of the Cr–Mo–Ti System. Journal of Phase Equilibra and Diffusion 45, p. 433 (2024)
Stein, F.; He, C.: About the Alkemade Theorem and the Limits of its Applicability for the Construction of Ternary Liquidus Surfaces. Journal of Phase Equilibra and Diffusion 45, pp. 489 - 501 (2024)
Gedsun, A.; Stein, F.; Palm, M.: Phase Equilibria in the Fe-Al-Nb(-B) System at 700 degrees C. Journal of Phase Equilibra and Diffusion 43 (4), pp. 409 - 418 (2022)
Distl, B.; Hauschildt, K.; Rashkova, B.; Pyczak, F.; Stein, F.: Phase Equilibria in the Ti-Rich Part of the Ti–Al–Nb System-Part I: Low-Temperature Phase Equilibria Between 700 and 900 °C. Journal of Phase Equilibra and Diffusion 43, pp. 355 - 381 (2022)
Distl, B.; Hauschildt, K.; Pyczak, F.; Stein, F.: Phase Equilibria in the Ti-Rich Part of the Ti–Al–Nb System-Part II: High-Temperature Phase Equilibria Between 1000 and 1300 °C. Journal of Phase Equilibra and Diffusion 43, pp. 554 - 575 (2022)
Gedsun, A.; Stein, F.; Palm, M.: Development of new Fe–Al–Nb(–B) alloys for structural applications at high temperatures. MRS Advances 6, pp. 176 - 182 (2021)
Stein, F.; Leineweber, A.: Laves phases: a review of their functional and structural applications and an improved fundamental understanding of stability and properties. Journal of Materials Science 56, pp. 5321 - 5427 (2021)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
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.