Žeradjanin, A. R.; Topalov, A. A.; Cherevko, S.; Keeley, G. P.: Sustainable generation of hydrogen using chemicals with regional oversupply - Feasibility of the electrolysis in acido-alkaline reactor. International Journal of Hydrogen Energy 39 (29), pp. 16275 - 16281 (2014)
Grote, J.-P.; Žeradjanin, A. R.; Cherevko, S.; Mayrhofer, K. J. J.: Coupling of a scanning flow cell with online electrochemical mass spectrometry for screening of reaction selectivity. Review of Scientific Instruments 85 (10), 104101 (2014)
Žeradjanin, A. R.: Impact of the spatial distribution of morphological patterns on the efficiency of electrocatalytic gas evolving reactions. Journal of the Serbian Chemical Society 79 (3), pp. 325 - 330 (2014)
Žeradjanin, A. R.; Menzel, N.; Schuhmann, W.; Strasser, P.: On the faradaic selectivity and the role of surface inhomogeneity during the chlorine evolution reaction on ternary Ti–Ru–Ir mixed metal oxide electrocatalysts. Physical Chemistry Chemical Physics 16 (27), pp. 13741 - 13747 (2014)
Ledendecker, M.; Mondschein, J. S.; Žeradjanin, A. R.; Cherevko, S.; Geiger, S.; Schalenbach, M.; Schaak, R. E.; Mayrhofer, K. J. J.: Stability of binary metallic ceramics in the HER reaction - feasible HER electrocatalysts in acidic medium? In Abstracts of Papers of the American Chemical Society, 254, 350. 254th National Meeting and Exposition of the American-Chemical-Society
(ACS) on Chemistry's Impact on the Global Economy, Washington, DC, August 20, 2017 - August 24, 2017. (2017)
Grote, J.-P.; Žeradjanin, A. R.; Cherevko, S.; Mayrhofer, K. J. J.: Electrochemical CO2 Reduction: A Combinatorial High-Throughput Approach for Catalytic Activity, Stability and Selectivity Investigations. International Conference on Combinatorial Materials Research, Ghent, Belgium (2015)
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…