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)
Woods, E.; Aota, L. S.; Schwarz, T.; Kim, S.-H.; Douglas, J. O.; Singh, M. P.; Gault, B.: In-situ cryogenic protective layers and metal coatings in cryogenic FIB. IMC20 - 20th International Microscopy Congress - Pre-congress workshop, Cryogenic Atom Probe Tomography, Busan, South Korea (2023)
Schwarz, T.: Atom probe tomography: from water to complex liquids to the application of studying liquid-solid interfaces at the near atomic level. APT&M 23, Leuven, Belgium (2023)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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…