With the help of ab-initio molecular dynamics simulations we were able to show that it is possible to use femtosecond laser pulses to induce the ultrafast nonthermal formation of NV-centers in diamond. NV centers are of fundamental importance in quantum technologies.

The ESA Gaia satellite mission delivers ultra-high precision data for astronomy and fundamental physics. Converting the raw data to a usable form is one of the largest computational challenges ever solved in observational astronomy. The local astronomy group at TUD is responsible for the core computations, calibration and relativistic modeling of the data and part of the European Gaia data consortium. The usage of the local HPC system is absolutely essential for this work.

Accurate parameter-free calculations of optical response functions for real materials and nanostructures still represent a major challenge for computational materials science. Our project focusses on the development and application of efficient but accurate ab-initio methods that give access to the linear and nonlinear optical spectra. We explore, on the atomistic level, how the material structure, its composition and defects, but also external parameters like stress, temperature or magnetic fields influence the optical response. It thus leads to a better understanding of existing materials and contributes to the design of new photonic materials.

The interaction of light with matter covers a large number of physical phenomena that we literally see in our everyday life. Early scientists mostly focused on investigations of electromagnetic radiation in the visible range and at low intensities, where material polarization responds linearly to incident electromagnetic fields. Utilizing the compute clusters at PC2, this project aims at simulating and interpreting the strong-field dynamics of real molecules and larger systems in a rigorous real-space real-time approach including non-linear strong-field effects such as photoionization and high-order harmonic generation of systems ranging from small (chiral) molecules over nano-systems to the condensed phase.

Bringing data from various sources together, poses severe challenges to their interoperability. A prerequisite to using such data together, e.g. in machine-learning tasks, requires the assessment of the data quality. The project described here, aims at doing so by deriving trust levels for data from density-functional theory (DFT). A trust level shall be assigned for a material based on what approximation (density functional) and what numerical settings were used in the DFT simulation.