Huang, S.; Tegg, L.; Yamini, S. A.; Tuli, V.; Burr, P.; McCarroll, I.; Yang, L.; Moore, K. L.; Cairney, J. M.: Atom probe study of second-phase particles in Zircaloy-4. Journal of Nuclear Materials 616, 156049 (2025)
Huang, S.; Tegg, L.; Yamini, S. A.; Chen, L.; Burr, P.; Qu, J.; Yang, L.; Mccarroll, I.; Cairney, J. M.: Atomic distribution of alloying elements and second phase particles (SPPs) identification in Optimised ZIRLO. Acta Materialia 297, 121365 (2025)
Kubásek, J.; Torkornoo, S.; Nečas, D.; McCarroll, I.; Hybášek, V.; Gault, B.; Jablonská, E.; Donik, Č.; Paulin, I.; Gogola, P.et al.; Kusý, M.; Míchal, Z.; Fojt, J.; Čavojský, M.; Duchoň, J.; Jarošová, M.; Čapek, J.: Towards increased strength and retained ductility of Zn-Mg-(Ag) materials for medical devices by adopting powder metallurgy processing routes. Journal of Materials Research and Technology 37, pp. 4345 - 4361 (2025)
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)
Yang, L.; Chen, E. Y.-S.; Qu, J.; Garbrecht, M.; McCarroll, I.; Mosiman, D. S.; Saha, B.; Cairney, J. M.: Improved atom probe specimen preparation by focused ion beam with the aid of multi-dimensional specimen control. Microstructures 5 (1), 2025007 (2025)
Torkornoo, S.; Bohner, M.; McCarroll, I.; Gault, B.: Optimization of Parameters for Atom Probe Tomography Analysis of β-Tricalcium Phosphates. Microscopy and Microanalysis 30 (6), pp. 1074 - 1082 (2024)
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)
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
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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…