Todorova, M.; Surendralal, S.; Wippermann, S. M.; Deißenbeck, F.; Neugebauer, J.: Processes at solid/liquid interfaces – insights from ab initio molecular dynamics simulations with potential control. AMaSiS 2021 Online - Applied Mathematics and Simulation for Semiconductors and Electrochemical Systems, Berlin, Germany (2021)
Todorova, M.; Surendralal, S.; Wippermann, S. M.; Deißenbeck, F.; Neugebauer, J.: Insights into processes at electrochemical solid/liquid interfaces from ab initio molecular dynamics simulations. ICTP-Workshop on “Physics and Chemistry of Solid/Liquid Interfaces for Energy Conversion and Storage”, Virtual Meeting, Trieste, Italy (2021)
Deißenbeck, F.: Development of an ab initio electrochemical cell: Understanding the dielectric properties of interfacial water and Mg dissolution from first principles. Dissertation, Philipps-Universität Marburg, Germany (2024)
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
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.