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)
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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.