Todorova, M.: Future directions in materials from modelling. Future directions in materials research in Europe organised by the Materials Australia VIC-TAS Branch/RMIT Europe, Online (2024)
Todorova, M.; Surendralal, S.; Deißenbeck, F.; Wippermann, S. M.; Neugebauer, J.: Ab Initio Calculations for electrified solid/liquid interfaces – Challenges, insights and Opportunities. GRC Aqueous Corrosion: Corrosion Challenges and Opportunities for the Energy Transition, New London, NH, USA (2024)
Neugebauer, J.; Deißenbeck, F.; Wippermann, S. M.; Todorova, M.: Getting the Electrochemical Interface into an Ab Initio Supercell. CECAM workshop "Electrochemical Interfaces in Energy Storage: Advances in Simulations, Methods and Models", Lausanne, Switzerland (2024)
Todorova, M.; Surendralal, S.; Yang, J.; Neugebauer, J.: Using ab initio calculations to unravel atomistic processes at electrified solid/ liquid interfaces. 63rd Sanibel Symposium, St. Augustine, FL, USA (2024)
Todorova, M.; Surendralal, S.; Deißenbeck, F.; Wippermann, S. M.; Neugebauer, J.: Insights into Electrified Solid/Liquid Interfaces from Ab initio and Atomistic Molecular Dynamics Simulations. CECAM - Young Researchers' School on Theory and Simulation in Electrochemical Conversion Processes, Paris, France (2023)
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.