Hickel, T.; Freysoldt, C.; Janßen, J.; Wang, N.; Zendegani, A.: High-throughput optimization of finite temperature phase stabilities: Concepts and application. Coffee with Max Planck, virtual seminar organized by the MPIE, Düsseldorf, Germany (2021)
Freysoldt, C.: Modelling of charged point defects with density-functional theory. 4th International Workshop on Models and Data for Plasma-Material Interaction in Fusion Devices, National Institute for Fusion Science (NIFS), Toki, Japan (2019)
Freysoldt, C.: Ab initio simulations of charged surfaces. Workshop “High electric fields in electrochemistry and atom probe tomography", Ringberg Castle, Germany (2017)
Dehm, G.; Harzer, T. P.; Dennenwaldt, T.; Freysoldt, C.; Liebscher, C.: Chemical demixing and thermal stability of supersaturated nanocrystalline CuCr alloys: Insights from advanced TEM. MS&T '16, Materials Science & Technology 2016 Conference & Exhibition, Salt Lake City, UT, USA (2016)
Freysoldt, C.: Accurate thermodynamic properties from ab initio simulations. International Conference on Theoretical and High Performance Computational Chemistry 2015, Qingdao, China (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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.