Research

The project studies the spectroscopic signatures of molecular clusters and ferroelectric solid solutions with extreme non-linear optical properties. It examines how atomic and electronic structure, chemical composition, and their interactions influence these signatures. Using first-principles modeling, atomistic calculations are performed within the density functional theory (DFT) framework and advanced methods like hybrid-DFT, time-dependent DFT, and many-body perturbation theory. Prototypical systems such as adamantane- or cubane-shaped clusters and crystalline solids are investigated to identify the prerequisites for optical non-linearities, guiding the synthesis of new compounds with tailored optical properties.

In current collider experiments and in particular in upcoming ones, like the Electron Ion Collider at the Brookhaven National Laboratory at New York, the structure the constituents of nuclei, i.e., protons and neutrons, are (and will be) extensively studied. While we know protons and neutrons are made of quarks and gluons, we know little about how these building blocks are arranged. And while protons and neutrons make up the bulk of everything we see in the universe, their constituent quarks account for only a small fraction of their mass. Although being massless, gluons are in fact responsible for more than 90 percent of the mass of visible matter in the universe. These gluons generate the so-called strong force, one of the four

TNG-Cluster is a cosmological magnetohydrodynamical simulation of cosmic structure formation, from shortly after the Big Bang until the present day. It self-consistently solves the coupled equations of self-gravity and MHD within an expanding spacetime. It simulates several hundred galaxy clusters – the most massive gravitationally bound objects in the Universe, each with a mass of roughly 10^15 times the mass of the Sun. TNG-Cluster resolves the multi-scale interplay of astrophysics processes, from gas cooling and turbulence, to star formation, stellar evolution, supernovae explosions, to the formation of supermassive black holes and their powerful feedback energetics. It is a broad theoretical model that enables us to probe the (astro

For the simulation of turbulent dispersed liquid-liquid flows at large scales, coalescence and breakup of droplets is approximated with sub-grid scale closures. For these closures, the root mean square (RMS) droplet fluctuation velocity Urms,d is a decisive input quantity. Recently, Solsvik & Jakobsen [1] proposed an enhanced model to predict Urms,d, which has not been verified yet. Hence, Direct Numerical Simulations (DNS) together with a Volume-of-Fluid (VoF) approach were employed to study the motion of single droplets in a Forced Homogeneous Isotropic Turbulent (FHIT) flow. A parameter study was conducted to investigate the effect of the initial droplet diameter D on Urms,d, and the DNS results were used to assess the model from [1].

The world surrounding us is made of atomic nuclei and nuclear matter. In this project, we investigate such strongly interacting systems under extreme conditions, given by large pressures and densities as they are found in neutron stars and along the edges of the three-dimensional hypernuclear chart, where there is a strong competition between attractive and repulsive forces, requiring high-precision calculations to understand the emergence of the drip lines when protons, neutrons or hyperons are added to a given atomic nucleus.

Within Text+, the NFDI consortium dedicated to building and providing infrastructure for the field of digital humanities, high performance computing (HPC) is gaining ground quickly. With the arrival of large language models (LLMs), the motivation for providing HPC infrastructure increased decisively. As a consequence, the first HPC service was established, easing the way for developments in several other areas where access to HPC is needed for enabling solutions otherwise not feasible. Examples of HPC use in Text+ are the Text+ LLM service and the NLP tool MONAPipe.

OpenGPT-X has set a goal to create and train open large language models (LLM) for European languages. Existing language models focus primarily on the English language, and hence perform unfavourably when used for any of the other commonly spoken European languages.
From large-scale benchmarking of multilingual LLMs to introducing Teuken-7B models, our research uncovers how tokenization and balanced datasets enhance cross-lingual performance. Join us in exploring transparent and reproducible innovations shaping the future of multilingual AI.

Computational tools such as Molecular Dynamics (MD) have revolutionized the way we study biomolecules; however, they are severely limited by the computational cost of running simulations on biological time- and length-scales. Various coarse-grained (CG) models have been developed which rely on simpler representations of molecular systems than atomistic MD. While these models are difficult to configure using physical intuition, we have shown that by using state-of-the-art machine learning methods, it is possible to design accurate and efficient CG models which can correctly reproduce protein dynamics. By enhancing both our training dataset and network architecture, we hope to produce a “universal” CG model to study biological systems.

Converging calculations is a common need in the ab initio materials-science community.
This tedious and resource-intensive process can be largely avoided if well-validated
recommendations are available. In order to create a recommender system to assist
users, benchmark data are required. This project addresses this need. It evaluates the
convergence behavior of electronic properties for a dataset of 10 materials that are
promising for optoelectronic applications.

In this project the goal is to use algorithms to predict classes of the library classification system “Basisklassifikation” (which can be translated as basic classification). A library classification system is a taxonomy of predefined classes that represent disciplines, subdisciplines, themes or types of publications. Subject librarians assign one or more of these classes to each publication, allowing both final users or retrieval system to use this annotated information for finding publications. As input data we observe mainly bibliographic data, such as for example the title, the name of the publisher, the year of publication and the language of the publication. The algorithms should suggest several classes, which are then analyzed by