Sevlikar, S. V.; Muralikrishna, G. M.; Gaertner, D.; Starikov, S.; Brink, T.; Scheiber, D.; Smirnova, D.; Irmer, D.; Tas, B.; Esin, V. A.et al.; Razumovskiy, V. I.; Liebscher, C.; Wilde, G.; Divinski, S. V.: Grain boundary diffusion and segregation of Cr in Ni Σ11(1̄13)[110] bicrystals: Decoding the role of grain boundary defects. Acta Materialia 278, 120229 (2024)
Ahmad, S.; Brink, T.; Liebscher, C.; Dehm, G.: Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum. Acta Materialia 268, 119732 (2024)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion. Physical Review Materials 8 (6), 063602 (2024)
Garcia-Suarez, J.; Brink, T.; Molinari, J.-F.: Breakdown of Reye’s theory in nanoscale wear. Journal of the Mechanics and Physics of Solids 173, 105236 (2023)
Brink, T.; Pemma, S.; Choi, Y.; Dehm, G.: From the microscopic to the macroscopic grain boundary: The role of line defects. 2026 MRS Spring Meeting & Exhibit, Honolulu, HI, USA (2026)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.