Kim, S.-H.; Dong, K.; Zhao, H.; El-Zoka, A.; Zhou, X.; Woods, E.; Giuliani, F.; Manke, I.; Raabe, D.; Gault, B.: Understanding the Degradation of a Model Si Anode in a Li-Ion Battery at the Atomic Scale. The Journal of Physical Chemistry Letters 13 (36), pp. 8416 - 8421 (2022)
El-Zoka, A.; Kim, S.-H.; Khanchandani, H.; Stephenson, L.; Gault, B.: Advances in Cryo-Atom Probe Tomography Studies on Formation of Nanoporous Metals by Dealloying (Digital Presentation). In ECS Meeting Abstracts, MA2022-01 (47), p. 1983. The Electrochemical Society (2022)
El-Zoka, A.: Materials Degradation: Turning a problem into a solution. Colloquium on Integrated Metallic Nanomaterials Systems, Hamburg, Germany (2021)
El-Zoka, A.: Towards Enhanced Atom Probe Tomography Characterization of Wet Corrosion Systems. (SCI Electrochemistry Postgraduate Conference, Manchester, UK (2021)
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…