LATEST RESEARCH RESULTS

Materials are made of electrons (negative charges) and nuclei (positive charges). The hydrogen atom is the simplest case: one nucleus and one electron. The behavior of a single electron attracted by a positively charged nucleus is complex, but also well understood: it is the quantum-mechanical version of a planet rotating around the sun. Materials contain many electrons, however. When the number of electrons increases the behavior of a system can radically change. This is because electrons strongly interact with each other: they are all negative charges and thus they try to avoid one another. When electron-electron repulsion effects dominate their behavior, electrons lose their individuality, forming cooperative emergent states.…

In this project, researchers from Fraunhofer IWM and the University of Freiburg explored how materials respond when exposed to harsh mechanical or chemical conditions altering the chemical structure close to the surface. This can lead to high friction and wear but also to unexpected effects or even to beneficial materials modifications. Applications range from machine tools, triboelectricity to functional materials for solar water splitting devices. Large scale quantum mechanical simulations revealed, among other findings, how to predict and understand friction under high pressure - where chemical bonds continuously break and reform - how to hydrogenate titanium dioxide (TiO2) efficiently at room temperature to enhance its functional…

The SunGANBoost project developed advanced deep learning models capable of generating realistic synthetic solar images from multi-wavelength observations. By training large Generative Adversarial, Autoencoder and Diffusion models on data from NASA’s Solar Dynamics Observatory using the JUWELS Booster GPU supercomputer, the team achieved unprecedented fidelity in reproducing solar structures across wavelengths. The resulting models will support future solar physics research, helping scientists better understand and predict solar activity and its effects on space weather.

Researchers from Jülich and University of Milan Bicocca investigated how microorganisms protect themselves from the toxic effects of fluoride. They focused on a protein called Fluc, which creates a tunnel in the membrane to pump fluoride out from bacterial cells. One of the tunnel stations for fluoride contains a protein amino acid, glutamate, which can be negatively charged, as fluoride is. However, two close negative charges would repel each other and bring Fluc to a halt. Such jam could be resolved by either lifting the glutamate barrier out of the way or paying the toll of protonating the glutamate. Preliminary results from multiscale molecular dynamics simulations suggest that both options are feasible.

Researchers at the Institute of Theoretical Physics, Chinese Academy of Sciences, used the JUWELS supercomputer at Jülich Supercomputing Centre to explore how intense laser pulses interacting with plasma can produce spin-polarized particle beams. By performing large-scale particle-in-cell simulations that track both particle motion and spin dynamics, the project uncovered how magnetic fields, radiation effects, and plasma inhomogeneities shape spin polarization—insights that may guide future experiments and applications in high-energy physics and astrophysics.

Zinc(II)-binding proteins play essential roles in biology, but their complex metal coordination is difficult to model accurately. Using an accurate Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) approach, this study explored the zinc(II) site of the Histone deacetylase protein, revealing detailed electronic and coordination dynamics. Leveraging our in-house MiMiC software on the JUWELS supercomputer, large-scale QM/MM MD simulations were performed efficiently providing insights into metal-ligand interactions that advance our understanding of metalloproteins.

This project uses quantum-powered computer simulations to improve how new drugs are discovered. Traditional simulation methods often fail for complex proteins, especially those with metals or chemical reactions. By combining quantum and classical physics in the newly developed MiMiC framework and running on the JUWELS supercomputer at Jülich, a new Quantum HPC Virtual Screening (QHPC–VS) method was developed. Applied to the mutant IDH1 enzyme linked to brain cancer, it identified 15 potential PET imaging tracers, offering new tools for faster, non-invasive diagnosis.

Within the project we numerically investigated the two-dimensional Bose-Hubbard model with local onsite disorder, where the competition between disorder and short-range interactions leads to the emergence of a Bose Glass (BG) phase between the Mott Insulator (MI) and superfluid (SF) phases [1]. To solve the inhomogeneous system, we employed real-space bosonic dynamical mean-field theory [2], which maps the complicated many-body problem to a collection of numerically solvable impurity models. Within our approach we always find an intermediate BG phase between the SF and MI. Analyzing the spectral function in the strong coupling regime reveals evidence for analytically predicted damped localized modes in the dispersion relation [5].

Critical phenomena occur at the onset of continuous phase transitions, where universality emerges: some observables are independent of the details of the local interactions, and are rather determined by global features, thereby defining universality classes. This project investigates critical phenomena in the presence of surfaces and defects, where rich phase diagrams are anticipated. It includes a quantitative study of the recently discovered extraordinary-log phase in the three-dimensional O(N) model, along with precise numerical estimates of universal coefficients for the three-dimensional Ising model with boundaries.

Mega radio halos are Millions of light years extended and faint radio sources which illuminate the rarefied medium at the extreme periphery of clusters of galaxies, which have been discovered only in 2023 and whose origin is unknown.

Using high-resolution simulations performed on the JUWELS supercomputer, researchers from the University of Bologna might have for the first time modelled how such giant emission regions might form, based on the idea that the turbulence of present in between galaxies can give a fraction of its energy to electrons, making them moving at the speed of light and emit synchrotron radiation on scales never seen before.

Dwarf galaxies are the smallest and most numerous galaxies, offering a clear view of fundamental astrophysical processes. Their shallow gravitational potentials make them highly sensitive to stellar feedback, helping us understand how feedback processes regulate star formation and the development of the multi-phase interstellar medium (ISM). They also preserve clues about early galaxy formation, chemical enrichment, and the nature of dark matter, serving as vital laboratories for testing cosmological models. In this project simulations of dwarf galaxies were performed to investigate the impact of stellar feedback and of the galactic environment including e.g. shearing motions on the ISM.

Prof. Dr. Holger Gohlke and his team used the compute resources of the Jülich Supercomputing Centre to study the carboligase ApPDC and the transaminase Cv2025, enzymes of high industrial interest, as biocatalysts to produce fine chemicals to be used as agrochemicals or pharmaceutical compounds. The team performed extensive molecular dynamics simulations for enzyme variants in different solvents compositions and used Constraint Network Analysis (CNA) to simulate the thermal unfolding process of the enzymes in a rigid cluster decomposition.

Prof. Dr. Holger Gohlke, Pablo Cea Medina, and Alena Endres used the computing resources of the Jülich Supercomputing Centre to study the substrate specificity of esterases. Esterases are enzymes with multiple biotechnological applications, as they can degrade a wide variety of substrates. However, finding which esterase can degrade which substrates is hard, expensive, and time-consuming. Therefore, the team seeks to develop a rational understanding of how different esterases recognize their substrates, to effectively predict which esterase would be suitable for a given task.

Autonomous vehicles must predict the motion of other road users within fractions of a second in complex urban environments with hundreds of lanes, traffic lights, and vehicles. RedMotion addresses this challenge through a novel transformer architecture that learns augmentation-invariant and redundancy-reduced descriptors of road environments. By compressing up to 1,200 local environmental features into exactly 100 compact tokens through self-supervised learning, RedMotion achieves efficient and accurate motion prediction. Training on millions of traffic scenes from the Waymo and Argoverse datasets required extensive parallel computations on the GPU nodes of JUWELS Booster.

Deep learning is revolutionizing protein science, with graph neural networks (GNNs) and multimodal models enabling unprecedented insights into protein function and design. In this project, the team led by Prof. Dr. Holger Gohlke developed two complementary AI models: TopEC and OneProt. TopEC uses 3D GNNs to predict enzyme functions directly from protein structures, incorporating atomic distances and angles to achieve high accuracy across more than 800 enzyme classes. Its structure-aware approach outperforms traditional 2D methods and remains robust even when binding site information is uncertain. In parallel, OneProt extends the multimodal ImageBind framework to proteins, aligning structural, sequence, text, and binding data into a shared…

Plants may not have brains, but they do have glutamate receptor–like proteins (GLRs) that behave much like the ion channels driving learning and memory in animals. These mysterious channels regulate key plant functions, from nitrogen use to pollen growth, yet how they control ion flow has remained unclear. To uncover their secrets, researchers led by Prof. Dr. Holger Gohlke combined high-precision modeling and molecular dynamics simulations of moss GLRs. Using AlphaFold2, they built accurate 3D channel structures and revealed how subtle changes in pore residues reshape ion permeability. The mutant channel showed enhanced calcium flow, linked to a more electronegative pore, an insight confirmed by large-scale simulations on supercomputers.…

The influence of compressibility effects on wall bounded flows is still not fully understood, especially when investigating its interplay with methodologies of drag reduction in engineering type flows. This project dealt with the application of oscillation control to supersonic turbulent channel flow. This method, well investigated for incompressible flow, was analyzed with respect to the influence of compressibility on the control effectiveness, by varying Reynolds and Mach numbers or adding tailored dissipation terms, to separate the effect of intrinsic and variable property compressibility effects. Additionally, the flow control was seen to strengthen the effect of the so-called very large anisotropic scales (VLAS).

Neutron stars are the most compact objects in the Universe with typically 1.5 times the mass of our Sun compressed into a sphere of just about 25 km in diameter, implying central densities higher than those in atomic nuclei. Most neutron stars are formed as remnants of massive stars when the degenerate core of these stars becomes gravitationally unstable and collapses, while most of the stellar matter is ejected in a violent supernova explosion with velocities up to 10,000 km/s. Two such neutron stars in a binary system can collide in a violent merger event after having approached each other on a spiral orbit over hundred of millions to billions of years, driven by the continuous emission of gravitational waves.

Stars are cosmic fusion reactors, which gain energy by nuclear reactions of light atomic nuclei to heavier ones. In stars of more than about nine solar masses, a sequence of burning phases thus assembles successively heavier chemical elements, starting from hydrogen fusion to helium as in the Sun, and continuing with helium, carbon, neon, oxygen, and silicon burning until a core of iron builds up at the center of the star. Iron as the atomic nucleus with the highest binding energy per nucleon cannot produce energy by further burning, and thus the growing iron core cannot escape a catastrophic end.

The strong interactions as part of the Standard Model of particle physics are described by Quantum Chromo-dynamics (QCD). Due to its strong coupling at typical energy scales in today’s Universe, predictions for strongly interacting matter, such as the one of the quark-gluon plasma, appearing in collisions of heavy nuclei at the Relativistic Heavy Ion Collider (RHIC) and future efforts, cannot be obtained using perturbative methods. The numerical treatment of QCD, discretized on a spacetime lattice – lattice QCD – has proven to be a viable tool to investigate the properties of QCD in the strongly coupled regime.