Torkornoo, S.; Kang, Y.; Woods, E.; Wieland, D. C. F.; Zeller-Plumhoff, B.; Gault, B.; Schwarz, T.: In Vivo Degradation Pathways of Mg-10Gd Biodegradable Implants Revealed by Atom Probe Tomography. European Atom Probe Tomography Workshop, Marseille, France (2025)
Woods, E.; Schwarz, T. M.; Zhao, Y.; Gault, B.: Crystalline amino acid mass spectral fragmentation in different forms and under different conditions using atom probe tomography. European Atom Probe Tomography Workshop, Marseille, France (2025)
Torkornoo, S.; Kang, Y.; Woods, E.; Wieland, D. C. F.; Zeller-Plumhoff, B.; Gault, B.; Schwarz, T.: In Vivo Degradation Pathways of Mg-10Gd Biodegradable Implants Revealed by Atom Probe Tomography. 34th Annual Conference of European Society for Biomaterials, Turin, Italy (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)
Hsu, Y.-L.; Dumont, M.; Jang, K.; Gault, B.; Schwarz, T. M.: Understanding the structure of dinosaur enamel at a near-atomic scale. Advanced imaging techniques in biomineralisation research, Edinburgh, Scotland (2025)
Schwarz, T.; Hsu, Y.-L.; Dumont, M.; Garcia-Giner, V.; Jung, C.; Porter, A.; Gault, B.: Atom probe tomography – a new technique to understand biominerals/materials on the atomic scale. 8th BioMAT 2025 - Symposium on Biomaterials and Related Areas, Weimar, Germany (2025)
Zhou, X.; Hickel, T.; Gault, B.; Ophus, C.; Liebscher, C.; Dehm, G.; Raabe, D.: Exploring the Relationship Between Grain Boundary Structure and Chemical Composition at the Atomic Level. International Conference on Intergranular and Interphase Boundaries in Materials (IIB 2024), Beijing, China (2024)
Krämer, M.; Favelukis, B.; El-Zoka, A.; Sokol, M.; A. Rosen, B.; Eliaz, N.; Kim, S.-H.; Gault, B.: Compositional mapping of 2D MXenes at the near-atomic-scale by atom probe tomography. EUROMXENE Congress 2024, Valencia, Spain (2024)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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…