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)
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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…