Thissen, P.; Özcan, Ö.; Diesing, D.; Grundmeier, G.: Monte Carlo Simulation of Temperature Programmed Desorption Including Binding Energies and Frequency Factors Derived by DFT Calculations. 43rd Symposium on Theoretical Chemistry, Saarbrücken, Germany (2007)
Thissen, P.; Giza, M.; Grundmeier, G.: Adsorptionskinetik von Organophosphonsäure auf plasmamodifizierten Aluminiumoberflächen. 13. Bundesdeutsche Fachtagung für Plasmatechnologie, Bochum, Germany (2007)
Thissen, P.: Adsorption and Self-Organization of Organophosphonic Monolayers on Modi ed Oxide Covered Surfaces. Dissertation, Universitat Paderborn, Fakultät für Naturwissenschaften, Paderborn, Germany (2009)
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
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…
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.