Khan, T. R.; Vimalanandan, A.; Marlow, F.; Erbe, A.; Rohwerder, M.: Existence of a lower critical radius for incorporation of silica particles into zinc during electro-codeposition. ACS Applied Materials and Interfaces 4 (11), pp. 6221 - 6227 (2012)
Khan, T. R.; Erbe, A.; Auinger, M.; Marlow, F.; Rohwerder, M.: Electrodeposition of zinc-silica composite coatings: Challenges in incorporating functionalized silica particles into a zinc matrix. Science and Technology of Advanced Materials 12 (5), 055005 (2011)
Khan, T. R.; de la Fuenta, D.; Rohwerder, M.: Electrolytic co-deposition of SiO2 nanoparticles with zinc for improvement of corrosion protection. 59th Annual Meeting of the International Society of Electrochemistry, Seville, Spain (2008)
Khan, T. R.; Vimalanandan, A.; Rohwerder, M.; Marlow, F.: Electrodeposition of Zinc-Silica Coatings for Smart Corrosion Protection. EUROCORR 2011, the European Corrosion Congress “Developing Solutions For The Global Challenge”, Stockholm, Sweden (2011)
Khan, T. R.: Nanocomposite coating: Codeposition of SiO2 particles during electrogalvanizing. Dissertation, Fakultät für Maschinenbau der Ruhr-Universität Bochum, 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
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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…