Ahmad, S.; Liebscher, C.; Dehm, G.: Exploration of atomic structures in Σ3 [111] Al tilt grain boundaries. Sixth Conference on Frontiers of Aberration Corrected Electron Microscopy PICO 2021, virtual, Kasteel Vaalsbroek, The Netherlands (2021)
Ahmad, S.; Liebscher, C.; Dehm, G.: Strain-Induced phase transition in Σ3 [111] (211) tilt grain boundaries in Al. Microscopy conference Joint Meeting of Dreiländertagungn & Multinational Congress on Microscopy MC 2021, virtual, Vienna, Austria (2021)
Ahmad, S.; Meiners, T.; Frolov, T.; Liebscher, C.; Dehm, G.: Grain boundary structure and phase transitions in Cu and Al [111] tilt grain boundaries. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano, Düsseldorf, Germany (2019)
Ahmad, S.: Fundamental investigation of the atomic structures of [111] tilt grain boundaries, their defects and segregation behaviour in pure and alloyed Al. Dissertation, Ruhr-Universität Bochum (2023)
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
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.