Berova, V.; Hengge, K.; Burger, T.; Vega-Paredes, M.; Scheu, C.; Jurzinsky, T.: Accelerated stress test study on CO-tolerant PtRu anode catalysts for reformate PEM fuel cells. Journal of Power Sources 632, 236312 (2025)
Camuti, L.; Kim, S.-H.; Podjaski, F.; Vega-Paredes, M.; Mingers, A. M.; Acartürk, T.; Starke, U.; Lotsch, B. V.; Scheu, C.; Gault, B.et al.; Zhang, S.: Kinetics and direct imaging of electrochemically formed palladium hydride for efficient hydrogen evolution reaction. Physics > Chemical Physics (2025)
Cheraparambil, H.; Vega-Paredes, M.; Scheu, C.; Weidenthaler, C.: Unraveling the Evolution of Dynamic Active Sites of LaNixFe1-xO3 Catalysts During OER. ACS Applied Materials & Interfaces 16 (17), pp. 21997 - 22006 (2024)
Vega-Paredes, M.; Scheu, C.; Aymerich Armengol, R.: Expanding the Potential of Identical Location Scanning Transmission Electron Microscopy for Gas Evolving Reactions: Stability of Rhenium Molybdenum Disulfide Nanocatalysts for Hydrogen Evolution Reaction. ACS Applied Materials and Interfaces 15 (40), pp. 46895 - 46901 (2023)
Liang, Y.; Mrovec, M.; Lysogorskiy, Y.; Vega-Paredes, M.; Scheu, C.; Drautz, R.: Atomic cluster expansion for Pt–Rh catalysts: From ab initio to the simulation of nanoclusters in few steps. Journal of Materials Research 38, pp. 5125 - 5135 (2023)
Berova, V.; Garzón-Manjón, A.; Vega-Paredes, M.; Scheu, C.; Jurzinsky, T.: Influence of Shell Thickness on Durability of Ru@Pt Core-Shell Catalysts for Reformate PEM Fuel Cells. In ECS Meeting Abstracts, MA2022-01 (35), p. 1528. The Electrochemical Society (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
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…