Ankah, G. N.; Meimandi, S.; Renner, F. U.: Dealloying of Cu3Pd Single Crystal Surfaces. Journal of the Electrochemical Society 160 (8), pp. C390 - C395 (2013)
Valtiner, M.; Ankah, G. N.; Bashir, A.; Renner, F. U.: Atomic force microscope imaging and force measurements at electrified and actively corroding interfaces: Challenges and novel cell design. Review of Scientific Instruments 82 (2), pp. 023703-1 - 023703-8 (2011)
Renner, F. U.; Ankah, G.; Pareek, A.: Surface Morphology Changes during Dealloying. Pacific Rim Meetin on Electrochemical and Solid-State Science PRIME 2012 / ECS 222, Honolulu, HI, USA (2012)
Ankah, G. N.; Renner, F. U.; Rohwerder, M.: Fundamental Investigations of the Corrosion of Binary Alloys. 59th Annual Meeting of the International Society of Electrochemistry, Sevilla, Spain (2008)
Ankah, G. N.: Investigations of the Selective Dissolution of Cu3Au(111): In-situ and Ex-situ Characterization. Dissertation, Fakultät für Maschinenbau der Ruhr-Universität, Bochum, Germany (2011)
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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…