NHR Compute Time Projects

High precision calculations from lattice QCD are needed nowadays in many respects. In order to obtain such results it is mandatory to have good control over all systematic effects involved in such calculations. In this study we plan to investigate the systematic effects introduced by the finite volume of the system, which is important in particular for quantities like low energy constants, pseudoscalar masses and decay constants, or the axial charge of the nucleon.

Scientific field: 309 Particles, Nuclei and Fields

University: Universität Regensburg

Target system: parallel computer Fritz

The function of immune receptors expressed on the surface of a variety of cells is typically characterized by the sensing of an external signal, followed by signal modulation and transmission into the cell. All of these steps are directly or indirectly affected by the composition, structure, and characteristics of the plasma membrane that forms the signal transmission barrier, shapes the sites for both extracellular sensing and intracellular signaling cascades. In immune cells, these two are connected by immune receptors that bridge the membrane.

Immune receptors were repeatedly reported to be localized in specific membrane domains, so called immune domains, that involve mutual interactions between the receptors, co-receptors and the lipid environment. Clearly, immune cell activation depends on and may affect this domain formation. In this project we envisage to characterize the architecture and interaction dynamics of these immune domains, and their coupling to receptor signaling.

To investigate this mutual interplay and decipher the driving forces underlying immune domain formation, we will use both coarse-grained and atomistic molecular dynamics (MD) simulations of receptors and their environment in membranes of increasing complexity, from symmetric simplified model membranes to asymmetric membranes with a composition close to the plasma membrane. We expect the simulations to yield a fingerprinting of the lipid nanometer environment of immune receptors depending on the sequence as well as on the orientation of their transmembrane (TM) region within the membrane. The composition of the immune domain composition may also influence membrane shape such as curvature, which is as well an essential factor for the efficiency of binding of large ligands or of association and clustering of receptors. The mutual influence of the composition of immune domains and signaling-required receptor assemblies on the membrane shape will be screened employing coarse-grained MD simulations of a recently developed bicelle setup which is particularly suitable to study protein- and lipid-induced membrane reshaping processes in an unbiased manner.

In the long run, combining in silico molecular scale insights of immune domain composition, structure, and dynamics with results from immune cell activation, receptor localization and membrane phase in superresolution microscopy, and proteo-lipidomics, as provided by a collaborative network of experimental labs, will yield a comprehensive view on immune cell activation and its modulating key factors.

Scientific field: 201-02 Biophysics

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Correlation effects in the quantum mechanical many body problem are fascinating since there seems to be an unlimited richness in emergent phenomena. The common feature of all the subjects presented in this grant proposal is a model Hamiltonian that describes the physics at high energy. The aim is to understand the emergent low-lying and critical phenomena. Generically since the many body quantum mechanical problem does not allow for an exact analytical solution, there is no way to unambiguously elucidate the nature of the low lying excitations. Hence numerical simulations. The numerical simulations that we will carry out here are exact: for a given dimension of the Hilbert space (i.e. volume V ) and inverse temperature, β, we can carry out an unbiased calculation. However, since critical and emergent phenomena is very often a property of the thermodynamic limit, the bigger the system size the more conclusive and impactful our results. This essentially explains why we are dependent on supercomputing resources. In fact without access to supercomputing resources, we would not be able to address the questions posed in this proposal.

Our tool is a general implementation of the so called auxiliary field quantum Monte Carlo algorithm. This approach is triggered at solving systems of correlated electrons that couple to bosonic modes such as lattice vibrations. For a class of models that do not suffer from the so called negative sign problem, this approach allows us to compute properties of systems in thermodynamic equilibrium at polynomial cost. Generically, for short ranged interactions, the computation time scales as βV3.

Scientific field: 307-02 Theoretical Condensed Matter Physics

University: Julius-Maximilians-Universität Würzburg

Target system: parallel computer Fritz

This NHR project is tied to an existing DFG/AHRC-funded joint project by FAU Erlangen-Nürnberg and the University of Oxford studying the mechanisms used for disinformation, in particular viewpoint manipulation, by Russian state-sponsored media. The purpose of this sub-project is to create a dataset fully annotated with pose information of the English-language programs found on the now-banned YouTube presence of RT (Russia Today) both for the purpose of our own analysis and to create an annotated open dataset.

Scientific field: 104 Linguistics

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target systems: parallel computer Fritz & GPGPU cluster Alex

The goal of this project is to develop an unobtrusive wearable technology for long-term digestion monitoring, termed GastroDigitalShirt. We investigate low-amplitude bowel sounds (BS) as indicators of digestive disorders, including chronic inflammatory bowel diseases (IBD). We analyze methods to: (1) acquire BS in unconstrained environments (i.e. daily life of patients), (2) determine the benefit of source separation techniques to interpret BS sources, (3) spot BS patterns in continuous audio data streams, and (4) investigate how BS analysis can help patients and clinicians in the diagnosis and treatment of gut diseases.

Scientific field: 205-01 Epidemiology, Medical Biometry, Medical Informatics, 407-06 Biomedical Systems Technology, 409-07 Computer Architecture and Embedded Systems

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

The High Energy Stereoscopic System (H.E.S.S.) telescopes in Namibia observe the very-high-energy gamma-ray sky from 20 GeV to 10s of TeV. H.E.S.S. operates by observing the Cherenkov light emitted when such a gamma-ray creates a particle cascade in the atmosphere; the nature, origin and energy of the incident particle can be determined using image processing techniques. State-of-the-art deep learning methods allow for high-sensitivity image analysis at high-speed, making them an attractive analysis prospect for H.E.S.S.. With this project, we aim to develop a variety of new analysis methods for H.E.S.S. data, including improved identification of muonic events, exploration of geometric deep learning techniques, fast simulation approaches and new data augmentation strategies.

Scientific field: 311-01 Astrophysics and Astronomy; 309-01 Particles, Nuclei and Fields

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

The project “Detection and Attribution of climate change for the glaciers on Kilimanjaro: Targeting the processes at regional and local scales” is attempting to break down the concept of Detection & Attribution to the local scale, by using the case study of glacier change on Kilimanjaro. This is because (a) the long research record for this place and (b) the unique climate indicator potential of these glaciers on a freestanding peak in the tropical mid troposphere. Results will reveal how human influence on the climate is transferred to the site-specific level.

The project “Exploring the potential of coralline algae as climate proxy and for climate model evaluation: a Southern Hemisphere case study of New Zealand” aims to explore a novel climatic indicator, namely crustose coralline algae that grow in shallow ocean waters (CCA), for the purpose of improved global climate model (GCM) evaluation. In particular, we will target the improvement potential with regard to sea surface temperatures in the Southern Ocean and the effect of these ocean conditions on the regional high-mountain climate of New Zealand.

Scientific field: 313-01 Atmospheric Science

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz

GPCRSIM uses classical (force-field) molecular dynamics simulations to determine binding sites, binding free energies and activation/deactivation free-energy profiles for predominantly class A G protein coupled receptors. In order to be able to observe rare events such as binding or activation, metadynamics enhanced sampling simulations employ standard optimized simulation profiles that have been developed in predecessor projects. Multiple-walker simulations ensure high parallel performance in metadynamics simulations that use standardized collective variables and funnel constraints to restrict the

conformational space in the extracellular medium.

Scientific field: 201-02 Biophysics

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target systems: parallel computer Fritz & GPGPU cluster Alex

This project aims at designing tailor-made functionalization of magnetite nanoparticles to bind heavy metal ions and related organometallic compounds by means of molecular modelling and simulation. While at this stage reflecting a pure theory project, we aim at developing a model-based search strategy for identifying suitable constituents and structures as guides to syntheses. To achieve this, we build on a recently established concept for the removal of oil pollution from water that takes use of super-paramagnetic nanoparticles functionalized by self-assembled monolayers (SAMs) of n-alkyl phosphonic acids. While the association of hydrocarbons to the alkyl chains of such SAMs is driven by a simple hydrophobic segregation mechanism, well-chosen SAMs of tailor-made chelating residues are needed for efficiently binding and removing heavy metal pollutants from water. This calls for in-depth understanding of molecular recognition and the tuning of structural motifs – which shall both be achieved from molecular simulations.

Scientific field: 303-02 Theoretical Chemistry; 406 Materials Science

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target systems: parallel computer Fritz & GPGPU cluster Alex

How do we best represent objects and their variations for inverse rendering? Can a combination of classical and novel techniques increase photorealism whilst retaining a low dimensional and interpretable representation? And given such object models: How do we efficiently infer the scene graph from visual input based on object-agnostic and object-specific processing? And ultimately can we explain the extreme generalization capabilities of the human visual system with an inverse rendering account?

Scientific field: 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

In this project, we want to explore the computational power of HPC-Cluster for modelling single-channel patch-clamp data with Markov models. The 2D-Dwell-Time fit with simulations of time series captures gating kinetics with a high background of noise and can extract rate constants beyond the recording bandwidth. That makes the 2D-Fit exceptionally valuable for relating ion-channel kinetics with data from simulations of single molecules. In addition, 2D-distributions preserve the coherency of connected states. Thereby the algorithm can extract the full complexity of underlying models and distinguish different models.

Scientific field: 206-04 Systemic Neuroscience, Computational Neuroscience, Behaviour

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target systems: parallel computer Fritz & GPGPU cluster Alex

Ethylene (C₂H₄) is a building block for the production of polymers. The employed catalysts used for enabling the polymerization process are sensitive to impurities. C₂H₄ is usually produced by cracking light alkanes and contains acetylene (C₂H₂) which deactivates the catalyst. Therefore, it is important to reduce the amount of it before starting the polymerization reaction. This important issue can be alleviated by selective hydrogenation of C₂H₂ to C₂H₄. In order to achieve high control over selectivity, we can employ binary and ternary transition-metal alloy surfaces as catalysts. However, there are few known alloys for such reactions. Applying only extensive computational search and/or trial and error experimental methods hinders the catalytic search. We will apply an AI tool based on compressed sensing, in conjunction with DFT calculations to identify descriptors for predicting catalytic activity and selectivity from a relatively small set of training DFT data.

Scientific field: 303 Physical and Theoretical Chemistry; 307 Condensed Matter Physics

University: Berlin

Target system: parallel computer Fritz

The IDP “Measuring and Modelling Mountain glaciers and ice caps in a Changing ClimAte (M³OCCA)” aims to combine cutting edge technologies with climate research. We will develop future technologies and transfer knowledge from other disciplines into climate and glacier research. Within the doctoral project “Machine Learning on Radargrams”, we aim at using and modifying machine learning techniques from medical imaging as well as natural language processing and apply those to glaciological radargrams to extract information on ice thickness and internal structures of ice bodies.

Scientific field: 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

The design of proteins with tailored functions will tackle many societal challenges. Traditionally, the protein design process relied on searching the global minima of multidimensional energy functions, a process that required significant computational times for each run. Recent advances in NLP have produced protein language models capable of generating fit protein sequences within seconds. However, these current models lack control over the design process, thus preventing user-defined design. The proposal described here will train a joint model, ProtCTRL, to capture the sequence-function relationships. ProtCTRL will be capable of generating sequences upon a user prompt, ultimately enabling tailored protein design. The model will be publicly released.

Scientific field: 201-07 Bioinformatics and Theoretical Biology; 104-04 Applied Linguistics, Experimental Linguistics, Computational Linguistics

University: University of Bayreuth

Target system: GPGPU cluster Alex

The isolation of multiple bonded late transition metal complexes is a challenging task. Nevertheless, they are alleged key intermediates for important “every day” catalysis processes such as the oxidation of ammonia to nitric acid by O₂ (Ostwald process) or the catalytic depletion of toxic exhaust gases. Our calculations predict how to tame these elusive molecules to study them in the laboratory. Our work focused so far on terminal imido complexes of the late transition metals (Mn, Fe, Co, Ni, Ir, Pd, Pt) and has not only allowed to understand their intriguing electronic structure, but furthermore to demonstrate first applications in small molecular activation, energy conversion, catalysis, and photochemistry.
Similarly, we use computational methods to study organic redox systems and diradicals. We have predicted how to harness the peculiar properties of carbene decorated diradicals in solar cells and demonstrated their use as singlet fission molecules. Thus, our calculations helped to discover a new class of molecules of use for solar energy conversion, quantum computing, or organic light emitting diodes (OLEDs).

Scientific field: 321-01 Inorganic Molecular Chemistry; 321-02 Organic Molecular Chemistry; 323-01 Physical Chemistry of Molecules, Liquids and Interphases, Biophysical Chemistry

University: Saarland University

Target system: parallel computer Fritz

Using Molecular Dynamics (MD) simulations, it is possible to follow conformational changes in proteins at atomic resolution and at high time resolution. MD simulation studies can supplement experimental approaches that typically allow only the structural characterization of initial or final or average structures. Especially in case of conformational switching processes such as binding induced folding an understanding of the process requires the analysis of intermediate states and driving forces for conformational changes. We apply MD-simulations and advanced sampling techniques to understand the molecular details of conformational switching processes within the SFB1035 (“Control of protein function by conformational switching”). One focus is on the transition of a DNA binding domain from a partially unfolded state to a folded state upon binding to DNA. The simulations will be used to define arrangements at which the partners start to influence the conformational switching process and to identify associated energy barriers. As a second system we study the process of collagen folding which is process that involves the coupled folding of three peptide strands and association to form a stable triple helix conformation. The general goal of our simulation studies is to understand at atomic detail how a protein or DNA surface or a specific modification of a protein can help to promote transitions towards a folded conformation of a binding partner or protein segment.

Scientific field: 201-02 Biophysics

University: Technical University of Munich

Target system: GPGPU cluster Alex

GPCRs constitute the largest protein family in the human genome. This genome-encoded protein repertoire of about 1000 receptors is expressed in a tissue- and organ-specific manner and transduces a large variety of extracellular signals into the cell. Whilst key residues determining coupling specificity of G proteins have been localized at the Gα-C terminus (GαCT), e.g. of Gs or Gi or at the finger loop of arrestin, a common sequence motif of GPCRs responsible for specific recognition of different GαCT or finger loops has not been detected yet. The R*-Gs/i/o arrestin complexes resolved so far do not provide a clear explanation for G protein coupling specificity. Evidence from several sources suggests the existence of transient complexes between the R* and GTP- bound G protein that may represent several novel intermediates on the way to the formation of GαsGTP and may contribute to coupling specificity.

Scientific field: 201-07 Bioinformatics and Theoretical Biology; 201-02 Biophysics

University: Leipzig University

Target systems: parallel computer Fritz & GPGPU cluster Alex

Thymine DNA glycosylase (TDG) is an important enzyme involved in DNA repair, which removes mispaired or modified DNA bases and thus ensures genetic integrity. We have investigated possible reasons for its substrate specificity in our previous work and we now intend to extend the range of possible forms of the substrates to achieve a deeper understanding of the situation in the protein substrate complex prior to the chemical reaction. For this reason, we will investigate the possible role of imino-tautomeric forms of the damaged DNA bases flipped out into the enzyme active site as well as the effect of different protonation states of the substrate bases and an important histidine residue in the binding pocket.

Scientific field: 201-02 Biophysics

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

We aim to provide high-quality body models given medical imaging data by the segmentation of relevant structures. Fast 3D modelling is relevant in clinical-radiological everyday routine. In the automatic modeling part of a DFG project, current machine learning methods and atlas-based methods are to be compared for their segmentation proposals and combined to their strengths. A major goal of this project is the development of novel, fully automatic, group-oriented deep-learning and multi-atlas segmentation estimators for the highly efficient multi-organ and simultaneous tumor segmentation in medical 3D-CT image data sets.

Scientific field: 205-01 Epidemiology, Medical Biometry, Medical Informatics ; 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation

University: Aalen UAS

Target systems: parallel computer Fritz & GPGPU cluster Alex

Broadly neutralizing antibodies that bind to viral fusion proteins represent a promising strategy for protection from viral infections. Such antibodies can be used for passive immunization and are currently tested in clinical trials, but they are expensive and difficult to produce. As an alternative, antibody-derived peptides may be used for this purpose. In the present project, the complexes between antibodies and the viral fusion proteins from HIV-1 and CoV-2 are analyzed to identify energetic hot-spots of the interaction. This information will be used for the design of antibody-derived peptides that bind to viral fusion proteins thereby blocking viral infection. For that purpose, a computational pipeline is developed that uses molecular dynamics (MD) simulations to identify the most promising peptides for further experimental testing.

Scientific field: 201-02 Biophysics; 204-04 Virology

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Lipid nanoparticles (LNPs) are very successfully employed as novel transport vehicles for mRNA vaccines. A major gap in our understanding and thus obstacle for future developments of nanoparticle-mRNA drugs, however, is the lack of a molecular picture and molecular insight into LNPs. In this project we aim to provide unique insight at the atomistic scale into the structure and mechanisms of these carriers.

Scientific field: 201-02 Biophysics

University: Friedrich-Alexander-Universität Erlangen-Nürnberg

The design of new functional proteins can tackle many of the problems humankind is facing today but so far has proven very challenging. Analogies between protein sequences and human languages have been long noted and a summary of their most prominent similarities is described. Given the tremendous success of Natural Language Processing (NLP) methods in recent years, its application to protein research opens a fresh perspective, shifting from the current energy-function centered paradigm to an unsupervised learning approach based entirely on sequences. To explore this opportunity further we have pre-trained a generative language model on the entire protein sequence space. We find that our language model, ProtGPT2, effectively speaks the protein language and can generate de-novo sequences with natural properties in a matter of seconds.

Scientific field: 201-02 Biophysics

University: University of Bayreuth

The cost of energy produced by wind turbines has been undergoing a steady reduction. Wind energy supplied 15% of the electricity demand of the European Union in 2019. Since rotor blades are the determining component for both performance and loads, they are the objective of further optimizations. To obtain high efficiencies, an increased use of special aerodynamic profiles is observed possessing large areas of low-resistance, which means laminar flow is maintained. In order to design such profiles, it is necessary to include the laminar-turbulent transition in CFD simulations of wind turbine blades. Thus, the objective of the project is to carry out high-fidelity numerical simulations of the flow around a wind turbine blade at a realistic Reynolds number to get a deeper insight into this phenomenon and especially the transition process under different levels of the turbulence intensities of the approaching flow.

Scientific field: 404-03 Fluid mechanics

University: Helmut Schmidt University Hamburg

Scientific field: 404-03 Fluid mechanics

University: University of Bremen

Scientific field: 410-01 Physics

University: RWTH Aachen

Scientific field: 201-02 Biophysics

University: Leipzig University

Scientific field: 410-01 Physics

University: Julius-Maximilians-Universität Würzburg