Schwarz, T.; Birbilis, N.; Gault, B.; McCarroll, I.: Understanding the Al diffusion pathway during atmospheric corrosion of a Mg-Al alloy using atom probe tomography. Corrosion Science 252, 112951 (2025)
Woods, E.; Singh, M. P.; Kim, S.-H.; Schwarz, T.; Douglas, J. O.; El-Zoka, A.; Giulani, F.; Gault, B.: A versatile and reproducible cryo-sample preparation methodology for atom probe studies. Microscopy and Microanalysis, ozad120 29 (6), pp. 1992 - 2003 (2023)
Schwarz, T.; Yu, W.; Zhan, H.; Gault, B.; Gourlay, C.; McCarroll, I.: Uncovering Ce-rich clusters and their role in precipitation strengthening of an AE44 alloy. Scripta Materialia 232, 115498 (2023)
Schwarz, T.: Progress in the preparation of cryo-FIB samples from the liquid-solid interface for atom probe tomography. ThermoFisher FIB user meeting, Düsseldorf, Germany (2025)
Schwarz, T.; Wieland, F.; Zeller-Plumhoff, B.; Gault, B.: On the trail of Mg - Incorporation and diffusion of Mg into the bone structure during the biodegradation of a MgGd screw. 17th Biometal 2025 Symposium, Cetraro, Italy (2025)
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…