NHR Compute Time Projects
Current NHR compute time projects on Alex and Fritz at NHR@FAU
Humanities and Social Sciences | Life Sciences | Natural Sciences | Engineering Sciences | Completed NHR compute time projects
Humanities and Social Sciences
Linguistics
Pose22: Pose Estimation on Russian International News Media (10/2022–12/2023; LARGE SCALE)
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
Life Sciences
Basic Research in Biology and Medicine
ORmd: Systematic molecular dynamics simulations of the odorant receptor family (10/2023–09/2024; LARGE SCALE)
ORmd aims to explore the dynamics of all the odorant receptors (ORs). To achieve this, we will employ cutting-edge High-Throughput Molecular Dynamics simulations starting from receptor models in the active and inactive states. This work will allow us to generate for the first time a database of OR structures and molecular dynamics, shedding light on this large but unexplored GPCR subfamily. The analyses of the trajectories will allow us to outline structural features relevant to the activation, which will be common to all ORs or specific for some subgroups.
Scientific field: 201-02 Biophysics
University: TU Munich
Target system: GPGPU cluster Alex
ChannelProtonation: Investigating the Influence of Protonation States on Cation Conductivity in Ion Channels through Molecular Dynamics Simulations (10/2023–09/2024)
This project focuses on investigating the dynamics of proton-coupled cation permeation in ion channels, with a specific emphasis on comprehending how pH influences the channel’s cation conduction and selectivity. Our research falls under the framework of CRC1078, which aims to enhance our understanding of protonation dynamics in protein function by fostering collaboration among various experimental and computational groups. Within this project, we have set two primary objectives:
(i) To study the Influenza A M2 proton channel, a pH-activated viroporin known for exhibiting both proton and cation transport activities.
(ii) To investigate CNG channels, which are non-selective cation channels that play a pivotal role in the signal transduction pathways of vision and olfaction.
Our approach involves the utilization of classical molecular dynamics simulations and constant-pH simulations to model the protonation patterns of titratable residues within the channel pore and the cation conductance across various protonation states. We have used similar approach before to study AMPA receptor and K2P channels.
Scientific field: 201-02 Biophysics
University: TU Berlin
Target system: GPGPU cluster Alex
DNA-glycosylase: DNA Base excision repair (06/2023–06/2026)
DNA damages are a constant threat to the integrity of the genome. Damages of the DNA bases can arise from oxidation or aberrant methylation. Deamination of cytosine bases, for example, results in U:G mismatches which ultimately lead to mutations in the encoded protein. The Base excision repair system (BER) is a machinery of enzymes, recognising and removing damaged/wrong bases and replacing them with the correct one. While most base excision repair glycosylases usually only excise the base, it was shown that some also have lyase activity and thereby circumvent AP endonuclease which is otherwise responsible for this second step of the repair mechanism. This project aims for revealing the mechanism of this strand incision, how this relates to the rather narrow specificity of glycosylases with dual activity compared to the broader set of substrates in mono-functional glycosylases.
Scientific field: 201-02 Biophysics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
AmPeL: Interaction of Antimicrobial Peptide Lugdunin with Membranes (06/2023–07/2024; LARGE SCALE)
The AmPeL project seeks to advance our understanding of the novel antimicrobial peptide lugdunin by characterizing its interaction with various bacterial and eukaryotic membrane models. Specifically, the project aims to elucidate the mechanisms of action underlying lugdunin’s antimicrobial properties against bacteria, as well as investigate the reasons for its lack of activity against erythrocytes. By shedding light on the precise molecular interactions between lugdunin and different membrane types, this research could ultimately facilitate the development of new antimicrobial therapies that are both effective and safe for human use.
Scientific field: 201-02 Biophysics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz & GPGPU cluster Alex
AMPARGating: Investigation of cation gating and conduction mechanism of an AMPAR-type glutamate receptor (03/2023–06/2024)
The understanding of the molecular details behind the regulatory mechanisms of the AMPA-type glutamate receptor (AMPAR) is of high importance. The process of cation conduction in the AMPAR ion channel offers several points of action for modulation. The divers AMPAR family exhibits in general a huge plethora of different regulations und modulations of their activity. Some examples for those effects are stabilization of the open or closed state, slow down or facilitate desensitization, influence on the conductance for calcium ions, prevent or allow the ability to be blocked by polyamines and so on. Many of these effects are mediated by auxiliary proteins which bind to the core-receptor. The atomistic details behind these interactions are not fully understood. Thus, MD Simulation represent a versatile method to investigate questions like these. In this project, we apply all-atom MD Simulations to Cryo-EM structures of the AMPAR with bound auxiliary proteins to reveal the mechanisms by which the cation gating and conduction is modulated. Our focus will lie on a subunit composition which is thought to represent a stabilized open state and a different subunit composition which has an influence on the polyamine block. We aim to elucidate molecular interactions which are crucial for AMPAR regulation.
Scientific field: 201-02 Biophysics
University: Humboldt-Universität zu Berlin
Target system: parallel computer Fritz
Dynasome3: Exploring Protein Dynamics Space to Improve Protein Function Prediction (12/2022–12/2023; LARGE SCALE)
The function of proteins is determined by their amino acid sequence and tertiary structure, but nevertheless the particular function of most proteins is unknown. In the Dynasome project we explore to what extent protein function can be predicted by protein dynamics, and explore the space of protein dynamics in general. To this aim, we perform molecular dynamics simulations for a large set of 200 proteins. We analyze these simulations, using e.g. Markov state models, to capture a ‘dynamics fingerprint’ of the studied proteins. We suggest these dynamics fingerprints as a new tool for protein function prediction and for quantitative comparison of protein dynamics.
Scientific field: 201-02 Biophysics
University: Georg-August-Universität Göttingen
Target system: parallel computer Fritz
GPCRSIM: Metadynamics simulations of ligand binding/unbinding and receptor activation/deactivation for G-protein coupled receptors (10/2022–12/2023; LARGE SCALE)
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
CoupledFoldBind:Conformational presentation switching processes studied by Molecular Simulations (07/2022–06/2024)
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
SimMediSoft: Biomolecular simulations for the efficient design of lipid nanoparticles (07/2022–02/2024)
With the incredible growth in RNA therapeutics, lipid nanoparticles (LNPs) have become an indispensable tool to deliver RNA to target cells thereby providing promising perspectives to combat life-threatening diseases such as Amyloidosis or COVID-19. We use all-atom molecular dynamics simulations to resolve the structure of these clinically relevant particles and proved molecular insights how the RNA cargo is distributed inside the LNPs. The structural information gained from our simulations will help to guide the design of LNPs with improved properties.
Scientific field: 201-02 Biophysics
University: University of Augsburg
Target system: parallel computer Fritz & GPGPU cluster Alex
GPCRSCOMPEVO: Computational models of structure, dynamics and evolution of GPCRs (07/2022–12/2023; LARGE SCALE)
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
Antivirals: Structure-based design and optimization of ligands for novel antiviral strategies (04/2022–03/2024)
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
Plant Sciences
CEC: Convergent evolution of carnivorous plants (07/2022–09/2024)
Plant carnivory (i.e., plants feeding on trapped animals) has evolved independently at least six times. This repeated evolution implies that the genetic basis for plant carnivory is present in most – if not all – plants. By comparison of the genetic and transcriptomic landscape between carnivorous and non-carnivorous plants, we may be able to identify the common genetic elements required for plant carnivory. This will not only grant us insights into plant carnivory, but into plant evolution as a whole.
Scientific field: 202-01 Evolution and Systematics of Plants and Fungi
University: University of Wuerzburg
Target system: parallel computer Fritz
Medicine
MIA-NORMAL: Medical Image Analysis with Normative Machine Learning (10/2023–09/2028; LARGE SCALE)
As one of the most important aspects of diagnosis, treatment planning, treatment delivery, and follow-up, medical imaging provides an unmatched ability to identify disease with high accuracy. As a result of its success, referrals for imaging examinations have increased significantly. However, medical imaging depends on interpretation by highly specialized clinical experts and is thus rarely available at the front- line-of-care, for patient triage, or for frequent follow-ups. Very often, excluding certain conditions or confirming physiological normality would be essential at many stages of the patient journey, to streamline referrals and relieve pressure on human experts who have limited capacity. Hence, there is a strong need for increased imaging with automated diagnostic support for clinicians, healthcare professionals, and caregivers.
Machine learning is expected to be an algorithmic panacea for diagnostic automation. However, despite significant advances such as Deep Learning with notable impact on real-world applications, robust confirmation of normality is still an unsolved problem, which cannot be addressed with established approaches.
Like clinical experts, machines should also be able to verify the absence of pathology by contrasting new images with their knowledge about healthy anatomy and expected physiological variability. Thus, the aim of this proposal is to develop normative representation learning as a new machine learning paradigm for medical imaging, providing patient-specific computational tools for robust confirmation of normality, image quality control, health screening, and prevention of disease before onset. We will do this by developing novel Deep Learning approaches that can learn without manual labels from healthy patient data only, applicable to cross-sectional, sequential, and multi-modal data. Resulting models will be able to extract clinically useful and actionable information as early and frequent as possible during patient journeys.
Scientific field: 205-30 Radiology, Nuclear Medicine, Radiotherapy, Radiobiology; 205-12 Cardiology, Angiology; 409 Computer Science
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
Medchem-Dynamics: Molecular Dynamics and Docking Studies with Multifunctional Receptor-Ligand Complexes (01/2023–12/2023; LARGE SCALE)
The establishment of suitable molecular models, based on experimental target structures, is a prerequisite for the successful design of multifunctional drugs. In all approaches of the proposed CRC (TRR 351), such models help to understand relationships of structure and function, including the interactions of the individual modules. Modeling will also guide ligand optimization. Retrospectively, molecular models can support the rationalization of the observed biological responses and integrate novel structural information obtained by biophysical methods.
Our molecular models will be important
- to understand the origins of a drug’s pharmacological effect at the atomic level. Based on existing X-ray crystallography or cryo-EM structures, our multifunctional ligands bound to their target will be investigated by long-timescale molecular dynamics (MD) simulations.
- to guide the design and development of multifunctional drugs in all project areas of the CRC. The interaction quality and stability of envisaged ligand-target complexes will be evaluated by means of docking, energy minimization and MD simulations.
- to identify new chemotypes by virtual screening. Filtering ultra-large libraries (in the order of 109 compounds) with pharmacophore models and molecular docking paves the way to novel multifunctional ligands with non-canonical receptor-ligand interactions.
- to decipher the relationships between structure and function. The elucidation of multidimensional SAR analyses allows us to predict useful modifications of lead compounds to improve affinity, selectivity and functional properties.
- to predict potential cooperativity between the modules. MD simulations of target-bound multifunctional ligands compared to their target-bound modules will guide compound development.
- to establish design principles of multifunctional biopharmaceuticals. Simulations will evaluate their stability and structure and, thus, guide their design.
Scientific field: 205-8 Pharmacy
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
MASCARA: Molecular Assessment of Signatures ChAracterizing the Remission of Arthritis (11/2022–11/2023)
The classification of chest x-rays for radiologists is a time-consuming task. Recently, neural networks have been shown to be quite useful for a first assessment of chest x-rays. For this, a large amount of annotated data is necessary. Even though there are strong efforts to anonymize the patient identity, it still may be possible to identify a patient. Thus, in this work, we will investigate the generation of synthetic chest x- rays using latent diffusion models. This way, we will be able to generate large amount of data necessary to train chest x-ray classification networks while preventing leakage of patient identity.
Scientific field: 205-01 Epidemiology, Medical Biometry, Medical Informatics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
PatRo-MRI-2: Pathology-robust image reconstruction in Magnetic Resonance Imaging (10/2022–09/2026)
Medical imaging, particularly Magnetic Resonance Imaging (MRI), plays a pivotal role in contemporary healthcare, offering noninvasive diagnostic tools, guidance, and exclusive treatment monitoring and disease understanding options. The standard diagnostic medical imaging pipeline involves collecting raw data via scanner hardware, processing this data through image reconstruction algorithms, and subsequently analyzing it for pathology by radiology specialists. While this procedure has traditionally been optimized to produce visually interpretable images for human experts, it may inadvertently lead to the loss of essential patient-specific diagnostic information contained in raw sensor data.
This issue has been further compounded by recent advancements in machine learning for MRI reconstruction. Machine learning has the capacity to generate aesthetically pleasing images from minimal sensor data, which is beneficial for speedy imaging protocols that prioritize patient comfort. However, this reduction in data acquisition could cause generic image reconstruction techniques to obscure disease markers, replacing pathological features with typical healthy image features derived from the training data.
Machine learning has also shown remarkable proficiency in autonomously analyzing medical images and robustly identifying disease patterns across various modalities. However, these tools are generally detached from the image acquisition process, and are only applied to the reconstructed image data.
In this project, we aspire to amalgamate machine learning for image reconstruction and disease localization based on the image, thereby crafting an end-to-end learnable solution for image reconstruction and joint pathology detection that functions directly on raw measurement data. We predict this combination will amplify diagnostic accuracy, producing images that are ideally suited for both human experts and diagnostic machine learning models.
Our approach will lean on the capabilities of foundation models, with a particular focus on state-of-the-art diffusion models for image synthesis. This choice is motivated by their leading performance, but they require substantial GPU time for training, hence our request for more GPU compute time.
We’ve outlined three primary objectives for our project:
1. Developing a machine learning reconstruction method for accelerated MRI that withstands variations in scanners, sequences, and anatomy.
2. Designing disease detection techniques that can learn from human-generated annotations in image space while concurrently extracting information straight from accelerated raw measurement data.
3. Combining these developments to introduce end-to-end training of image reconstruction and image analysis.
We hypothesize that such an approach will prevent machine learning-based MRI interpretation from missing crucial disease indicators and offer tools to express interpretation uncertainties in image regions with limited sensor data. To this end, we will utilize our fastMRI+ dataset, which comprises fully sampled raw measurement data, images, and 20,000 specialist expert bounding box annotations for more than 50 pathology categories from over 8,000 patients.
Scientific field: 409 Computer Science;205-30 Radiology, Nuclear Medicine, Radiotherapy, Radiobiology; 205-12 Cardiology, Angiology
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
GastroDigitalShirt: Development and test of deep neural network models for the automatic detection of body sounds to monitor digestion in control group and patients with intestinal disorders (09/2022–11/2023)
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
FPRMetaD: Investigating binding pathways for a diverse set of ligands with biased and unbiased simulation of the Formyl Peptide Receptor (08/2022–10/2023)
Our project includes unbiased and biased molecular dynamics simulations of the FPR receptor class and its vast array of ligands that include modified peptides as well as non-modified peptides and small molecules like Lipoxin A4 or the circular peptide Ciclosporin A. A standardized metadynamics protocol is applied to study the binding/unbinding events of the ligands to these receptors and enhance sampling. High parallel performance is achieved using multiple-walker simulations. The ligands will include agonists, partial agonist and antagonist to cover as many different binding routes as possible.
Scientific field: 205-08 Pharmacy
University: Westfälische Wilhelms Universität Münster
Target system: parallel computer Fritz & GPGPU cluster Alex
InTimeVRSimulPatMod: In-time Virtual Reality Simulation Patient Models: Machine Learning and immersive-interactive Modeling of Virtual Patient Bodies (05/2022–05/2025)
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
Neurosciences
HPC-MarkovModelling: Single-channel Markov modelling of voltage-gated ion channels with simulations and implementation of the 2D-Fit algorithm on High Performance Computing Cluster (08/2022–07/2024)
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
Natural Sciences
Molecular Chemistry
SpectroscopicProperties: Spectroscopic properties of molecules with unusual electronic structures (07/2022–12/2025)
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
MoTrNanoMat: Molecular transport in nanoporous materials (07/2022–10/2025)
Nanoporous materials are of interest in many applications due to their high surface area and physicochemical properties. The interconnected channels can be used for “flow-through” applications such as purification of drinking water or nanoseparation of proteins or organic solvents. Here, we will evaluate the impact of (i) nanomaterial kind, (ii) pore size, (iii) pore shape, and (iv) solvent polarity on the material’s permeability using sequential multiscaling molecular dynamics (MD). We will establish molecular models of 3D structures of amorph carbonaceous materials and of diverse metal-organic frameworks. Next, variation of the pore diameter, surface functionalization and solvent polarity will enable us to systematically evaluate the impact of the individual material properties on e.g. molecular transport, solvent competition, and nanoseparation.
Scientific field: 301 Molecular Chemistry; 302-03 Theory and Modelling
University: University of Stuttgart
Target system: parallel computer Fritz & GPGPU cluster Alex
Chemical Solid State and Surface Research
Lg_SurfCat_AIMD_MLFF: Computational modeling of new surface catalysis systems by means of ab initio methods as well as novel machine-learning force-field approaches (01/2023–12/2023; LARGE SCALE)
Catalysis at liquid interfaces (CLINT) provides a fascinating new research area with great potential to explore more efficient and sustainable catalytic processes. Since such kind of catalysis, especially those on supported catalytically active liquid metal solutions (SCALMS) and surface catalysis with ionic liquid layers (SCILL), is still quite new, much more understanding need to be gained on how exactly the mechanistic processes are taking place, leading to know-how accelerating the development of targeted systems for several reactions. Periodic DFT simulations can shed a light on the exact processes taking place at the catalyst. Recently, a new approach to generate machine-learning force-fields (ML-FF) was developed which is able to efficiently learn on the fly from DFT data, leading to a high-level FF for metal surfaces in contact with other phases, which are very complicated to describe with FFs so far. By parametrizing these ML-FFs for SCALMS and SCILL systems, we can explore new time- and length scales.
Scientific field: 302-03 Theory and Modeling
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
SurfCatal_AIMD_MLFF: Computational modeling of new surface catalysis systems by means of ab initio methods as well as novel machine-learning force-field approaches (08/2022–12/2024)
Catalysis at liquid interfaces (CLINT) provides a fascinating new research area with great potential to explore more efficient and sustainable catalytic processes. Since such kind of catalysis, especially those on supported catalytically active liquid metal solutions (SCALMS) and surface catalysis with ionic liquid layers (SCILL), is still quite new, much more understanding need to be gained on how exactly the mechanistic processes are taking place, leading to know-how accelerating the development of targeted systems for several reactions. Periodic DFT simulations are able to shed a light on the exact processes taking place at the catalyst. Recently, a new machine-learning force-field (ML-FF) was developed which is able to efficiently learn on the fly from DFT data, leading to a high-level FF for metal surfaces in contact with other phases, which are very complicated to describe with FFs so far. By parametrizing these ML-FFs for SCALMS and SCILL systems, we are able to explore new time- and length scales.
Scientific field: 302-03 Theory and Modeling
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
MoTrNanoMat: Molecular transport in nanoporous materials (07/2022–10/2025)
Physical and Theoretical Chemistry
AKES: Chemical Modelling of Processes in Pharmaceutical Chemistry (11/2022–10/2023)
Covalent inhibitors currently experience a renaissance in medicinal chemistry due to their various advantages, including prolonged residence times, lower sensitivity against pharmacokinetic aspects, and high efficacy. Our work addresses the reaction mechanisms of cysteine protease rhodesain with covalent inhibitors. Our goal is to further improve the modelling of the underlying inhibition mechanisms so that we can suggest improved compounds. Furthermore, we aim to develop a protocol to treat enzyme-inhibitor reactions with various local minima.
Scientific field: 303-02 General Theoretical Chemistry
University: Julius-Maximilians-Universität Würzburg
Target system: GPGPU cluster Alex
Condensed Matter Physics
nqsQuMat: Neural quantum states for strongly correlated quantum matter (10/2023–09/2025; LARGE SCALE)
The efficient numerical simulation of quantum systems constitutes a key challenge for current computational methods. In this project, we employ a modern method named neural quantum states which has shown great potential in studying various quantum problems. We plan to target both ground states as well as the time evolution of cutting-edge quantum models to gain new insights on novel quantum phenomena.
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: University of Augsburg
Target system: GPGPU cluster Alex
SuperSEM: Simulation of unconventional superconductors with long-range interactions beyond the mean-field approximation (10/2023–09/2025)
Long-range interactions in unconventional superconductors are of high importance as they can give rise to topologically nontrivial phases of matter within the mean-field approximation. In this work, we use the T-matrix approach to derive a generalized gap equation that goes beyond the mean-field approximation. We subsequently investigate how the phase diagram of a 2D superconductor changes as higher-order quantum corrections are included. Going further, we explore whether the Higgs mode, which can be stabilized due to long-range interactions in the mean field limit, remains stable in the T-matrix approach.
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: Universität des Saarlandes
Target system: parallel computer Fritz
PCL-topology: Optical and vibrational signatures of structural and electronic topology in planar carbon lattices (07/2023–06/2026)
Planar carbon lattices (PCLs), such as graphene nanoribbons, patterned graphene, 2D metal- organic/covalent-organic frameworks (MOFs/COFs), 2D polymers and their nanoribbons, combine the versatility of carbon-based molecular building blocks with the long-range order and symmetry-imposed properties of a 2D lattice. In recent years, precision synthesis methods have made tremendous progress in creating predefined PCLs on surfaces or at liquid interfaces. In this project, we will investigate the physical properties of such PCLs by numerical methods and predict new PCL structures with intriguing topological and correlated electronic properties.
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
SELRIQS: Series expansions for long-range interacting quantum systems (06/2023–12/2024)
We investigate the quantum cooperativity of strongly correlated light-matter systems in the presence of spatially competing matter-matter interactions. We aim at a deeper understanding of quantum phases and quantum phase transitions, where competing long-range interactions are expected to result in unconventional correlations and interesting entanglement properties. To this end we employ linked- cluster expansions on white graphs in combination with classical Monte Carlo integration to determine relevant physical quantities in the thermodynamic limit. This allows us to determine the notoriously complicated quantum-critical properties in quantum many-body systems with long-range interactions.
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
UltrafastDyn: Ultrafast electron dynamics and Kerr rotations (01/2023–06/2025)
This project focuses on simulating and understanding topological materials and their excitations under ultrafast laser driving; it is motivated by experimental activities towards “lightwave spintronics” in Dirac systems. The main diagnostic observable will be the optical and magnetic responses, in particular the high harmonic regime and Kerr rotations.
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: University of Regensburg
Target system: parallel computer Fritz
NQSdynamics: Neural quantum states for dynamical processes in quantum matter (12/2022–11/2023)
The efficient numerical simulation of nonequilibrium real-time evolution in isolated quantum matter constitutes a key challenge for current computational methods. For further exploration of quantum dynamics, we employ the neural quantum state, which, as a new approach of encoding many-body wave functions, has shown great potential in studying various quantum phenomenon. Compared with existing numerical methods, it provides more flexibility and expressive power, which will probably reveal new insights towards microscopic nonequilibrium processes.
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: University of Augsburg
Target system: parallel computer Fritz
DMFT2TBLG: DMFT study of a heavy-fermion model for twisted bilayer graphene (12/2022–11/2023)
Twisted bilayer graphene (TBLG) has recently captivated the interest of the condensed matter community, for its capability of hosting a wide variety of peculiar phenomena such as superconductive and correlated insulating phases, as well as topological features. The aim of this project is to apply Dynamical Mean-Field Theory (DMFT) to a recently developed heavy-fermion model for TBLG, investigating the onset of correlated insulating phases for different occupations and temperatures, as well as dynamical screening effects.
Scientific field: 307 Condensed Matter Physics
University: Julius-Maximilians-Universität Würzburg
Target system: parallel computer Fritz
FRG: Functional Renormalization Group calculations for material analysis (11/2022–10/2023)
Current ab-initio theory for solid state materials excels in the prediction of electronic band structures. The secondary part of any full description – the interaction between electrons – is beyond the scope of those methods. To remedy this omission the functional renormalization group (FRG) has the expressed goal of deriving effective low-energy interaction models. This method has successfully been used to calculate some simple systems, but the goal of this project is to widen the scope of possible applications and include ever expanding classes of materials. We hope to uncover new effects such as pair-density waves, nematic instabilities and thus extend the predictive power of FRG.
Scientific field: 307-2 Theoretical Condensed Matter Physics
University: RWTH Aachen
Target system: parallel computer Fritz
ALFQMCsim: Emergent and critical phenomena in correlated electron systems: Quantum Monte Carlo simulations (10/2022–09/2026; LARGE SCALE)
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
Optics, Quantum Optics and Physics of Atoms, Molecules and Plasmas
DaREXA-F: Datenreduktion für Exascale-Anwendungen in der Fusionsforschung (06/2023–11/2025)
For the efficient usage of HPC applications in the exascale era, we need to improve the scalability on large and heterogeneous systems. This requires a variety of components, such as efficient processing, data storage, software, and algorithms. The goal of this BMBF-funded project is to develop new methods for reducing data traffic between compute nodes with distributed memory and storage in file systems on supercomputers. For this purpose, a co-design approach will be used to develop solutions for variable-precision computation, data compression and novel data formats. These solutions will be used to improve GENE, a program used worldwide for the simulation of plasma turbulence, and will be validated using GENE.
Scientific field: 308-01 Optics, Quantum Optics, Atoms, Molecules, Plasmas
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz & GPGPU cluster Alex
Particles, Nuclei and Fields
DPDa: Quark Double Parton Distributions of the Nucleon (04/2023–03/2024; LARGE SCALE)
A better understanding of Double-Parton Distributions (DPDs) in the proton is vital to make full usage of the discovery potential of the LHC (CERN). We have pioneered corresponding lattice simulations with quite encouraging results, and we want to perform additional simulations with different lattice constants, which will allow for a continuum extrapolation. Without a reliable continuum extrapolation lattice results have, strictly speaking, no physical relevance.
Scientific field: 309-01 Nuclear and Elementary Particle Physics, Quantum Mechanics, Relativity, Fields
University: Universität Regensburg
Target system: parallel computer Fritz
addlight: The spectrum of charmonium and glueballs: adding the light hadrons (01/2023–04/2026; LARGE SCALE)
Experiments at particle accelerators have discovered new particles called XYZ states, which do not fit into the spectrum of conventional mesons made of a charm and an anti-charm quark (charmonium). Many of the new states are close to thresholds for strong decays. States made of gluons only, so called glueballs, are predicted by the theory of strong interactions (Quantum ChromoDynamics) but their existence still awaits an experimental confirmation. In this project we plan to study charmonium and glueballs by simulations of QCD on a lattice. The novelty of our study is the inclusion of light hadrons into which these states can decay. Our software has excellent scaling behavior on HPC systems.
Scientific field: 309-01 Particles, Nuclei and Fields
University: University of Wuppertal
Target system: parallel computer Fritz
Statistical Physics, Soft Matter, Biological Physics, Nonlinear Dynamics
CLINT M02: Multiscale modelling of SILP and SCILL catalysis (07/2023–06/2025)
Project M02 focuses on multi-scale simulations of Interface-enhanced SILP and Advanced SCILL catalysis of hydrogenation reactions, to allow for intelligent design and optimisation of these technological systems. To this end, we will combine hybrid quantum mechanics calculations (chemical transformations), and molecular dynamics simulations (structuring & diffusivity), each thoroughly validated against the wealth of experiments performed in CLINT, to tune the functionality of the catalytic complex, the IL, and the support material. The aim is to identify, characterise and enhance effects, occurring on a multitude of molecular time and length scales, which favourably affect the overall reaction’s turn over.
Structuring of the IL and gas/liquid and liquid/solid interfaces creates a unique dynamic environment for the catalyst. We will therefore focus on developing an understanding of the structural and transport properties of functionalised ILs and reactant molecules within the film using molecular dynamics simulations. This will permit us to boost the spatial coordination of the reactants with the solid and the gas interfaces, and address issues of solubility, viscosity as well as wetting. Building on this understanding of the interface environment, we will devise a hybrid quantum mechanics/molecular mechanics (QM/MM) approach to embed local quantum characterisation of the reactive centre into inhomogeneous IL films.
Scientific field: 310 Statistical Physics, Soft Matter, Biological Physics, Nonlinear Dynamics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
CRC1411D04: Design of Particulate Products: Modelling particle aggregation and assembly into optimal structures (03/2023–02/2024)
Robust self-assembly of nanoparticles can create nanostructures that have applications as structural color pigments and scaffolds for heterogeneous catalysis. This project develops and applies new methods to study not only the self-assembly process itself but also the properties of the self-assembled nanostructures. The computations in this proposal closely interact with experimental work that is conducted in the framework of the Collaborative Research Centre Design of Particulate Products.
Scientific field: 310 Statistical Physics, Soft Matter, Biological Physics, Nonlinear Dynamics; 403/01 Chemical and Thermal Process Engineering
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz & GPGPU cluster Alex
MultiDyn: Dynamics of complex networks of oscillators (01/2023–12/2023; LARGE SCALE)
This project is designed to provide a wide-ranging stability phase diagrams classifying the distribution of self-organized periodicities and complex motions of various sorts in families of coupled nonlinear oscillators when more than one control parameter is varied simultaneously.
Scientific field: 310 Statistical Physics, Soft Matter, Biological Physics, Nonlinear Dynamics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
EnSimTurb: Towards ensemble simulations of fully developed turbulence (12/2022–04/2026)
The computing time allocation supports two ongoing third-party funded projects (ERC & DFG) in our group, in which we investigate the statistical properties of fully developed turbulence. In the ERC project, we aim at developing new theoretical and computational approaches to better understand and model fully developed turbulence. The goal of the DFG project is to capture the large-scale dynamics of turbulent flows. Both projects use our code TurTLE, a pseudo-spectral solver of the Navier-Stokes equations which also features particle tracking capabilities.
Scientific field: 310 Statistical Physics; 404-03 Fluid Mechanics
University: University of Bayreuth
Target system: parallel computer Fritz & GPGPU cluster Alex
Astrophysics and Astronomy
CosmosTNG: galaxy formation and evolution with constrained cosmological magnetohydrodynamical simulations at cosmic noon (07/2023–06/2024; LARGE SCALE)
CosmosTNG is a first-of-its-kind constrained cosmological hydrodynamical galaxy formation simulation. It is designed to explore the formation and evolution of galaxies within the large-scale structure of the high-redshift Universe. An observed patch of the sky is first used to reconstruct the density field of the early Universe, which is then evolved with a state-of-the- art structure formation code using the IllustrisTNG galaxy formation model. The outcome enables (i) a direct comparison between simulations and observations in the COSMOS field, (ii) a new tool to explore galaxy formation in constrained environments, and (iii) a probe of the physics and distribution of gas in and around galaxies – from the circumgalactic to intergalactic medium – at cosmic noon.
Scientific field: 311 Astrophysics and Astronomy
University: Heidelberg University
Target system: parallel computer Fritz
ECOGAL_MW: Full disk modeling of the Milky Way (05/2023–04/2024)
Does a young planetary system carry the imprint of its birthplace in the diversity of our Galactic ecosystem?
How does a specific environment or location in the Milky Way determines the properties of our Sun and solar system?
ECOGAL will create the essential synergic framework where the extensive observational datasets available for our Galaxy can be analyzed back-to-back with state-of-the-art theoretical simulations of all Galactic environments, also using innovative machine-learning and decision-making tools.
ECOGAL has the ambitious goal to build a unifying predictive model of the Milky Way ecosystem able to trace the properties of planet-forming disks back to the environmental conditions active in their birthplace.
This project interweaves the unique expertise of four European research groups at the forefront of science in the four pillars of astronomy and astrophysics: observations, theoretical simulations, instrumentation and data analysis.
Scientific field: 311 Astrophysics and Astronomy
University: Universität Heidelberg
Target system: parallel computer Fritz
Transient3body: Transient formation in three-body encounters between stars and black holes (03/2023–02/2024)
Many astrophysical environments, from stellar clusters to disks of active galactic nuclei, are characterized by frequent interactions between stars and stellar-mass compact objects. In particular, encounters involving three objects (three-body encounters) are frequent in dense stellar environments. Previous work on such encounters has mostly focused on understanding the orbit evolution and outcome assuming the encountering objects are point particles. However, the point-particle approximation cannot be used to study observable phenomena produced in those interactions where the size of the meeting objects and gas interactions between them become an essential role in generating electromagnetic radiation. Such phenomena include tidal disruption events of stars by black holes, which can have a significant impact on the black hole mass growth and the subsequent long-term interactions with the surrounding debris. To investigate the impact and outcomes of dynamical interactions we perform hydrodynamics simulations of various types of three-body encounters using the moving-mesh magnetohydrodynamics code AREPO.
Scientific field: 311 Astrophysics and Astronomy
University: Ludwig Maximilian University of Munich
Target system: parallel computer Fritz
HESSML: Advanced Machine Learning Analysis for the H.E.S.S. Telescopes (08/2022–01/2024)
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
Mathematics
Bio-FROSch: Modeling and simulation of pharmaco-mechanical FSI for an enhanced treatment of cardiovascular diseases and non-Newtonian micro-macro blood flow simulations (07/2023–06/2024)
This project delivers the necessary computing time for two different research projects, both concerned with modelling and simulation of different aspects of arteries and blood flow using the same software framework.
In the first project, we aim at the robust computational modeling of coupling fluid-structure interaction with pharmacological effects towards an enhanced treatment of cardiovascular diseases. In the future, this could lead to a virtual laboratory to assist medical doctors and patients in their decisions. The main goal is to develop a robust numerical framework including suitable models for the computational simulation of the effects of drugs on the complex bio-chemo-mechanical processes in arterial walls.
In the second project, we aim to develop an efficient multiscale model for blood flow simulation. The main focus is on implementing a computational fluid model combining two different scales: a macroscopic scale which models blood as a continuous fluid, and a mesoscopic/microscopic scale capturing the behavior of red blood cells (RBCs) within the flow. We aim to improve computational efficiency of the multiscale model by identifying and implementing areas where advanced machine learning techniques can be applied.
The development of robust numerical coupling schemes and solver strategies and the parallel implementation and integration thereof into the software libraries FEDDLib, FROSch, and Trilinos is essential for both projects.
Scientific field: 402 Mechanik; 312 Mathematik
University: Universität zu Köln
Target system: parallel computer Fritz & GPGPU cluster Alex
StroemungsRaum: Neuartige Exascale-Architekturen mit heterogenen Hardwarekomponenten für Strömungssimulationen (03/2023–09/2025)
Future Exascale computing architectures will feature a high number of heterogeneous hardware components that are comprised of special-purpose processors or accelerators. The corresponding realization of Computational Fluid Dynamics (CFD) application software as a central core component of nowaday’s CFD simulations in industrial context requires high-scaling methods. These methods solve high-dimensional and unsteady (non)linear systems of equations and need to be integrated into application software such that they can be used by non-HPC experts from the industry. Especially the open-source software FEATFLOW , is a central component of the StroemungsRaum platform that is successfully used by the industrial partner of the project IANUS for years. In the context of the whole project, FEATFLOW will be extended methodologically and by parallel near-hardware implementations.
Scientific field: 404-03 Fluid Mechanics; 312 Mathematics
University: TU Dortmund
Target system: parallel computer Fritz & GPGPU cluster Alex
Atmospheric Science, Oceanography and Climate Research
ATMOS: Numerical atmospheric modeling for the attribution of climate change and for model improvement (08/2022–03/2024; LARGE SCALE)
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
Physical Chemistry
SpectroscopicProperties: Spectroscopic properties of molecules with unusual electronic structures (07/2022–12/2025)
Theoretical Chemistry
ELTRANS: Electron transfer in organic and inorganic light-converting systems (10/2022–09/2026)
The project aids the design of new electrodes for photoelectrochemical water oxidation by studying the underlying electronic processes. It collaborates with experimental partners in Sonderforschungsbereich 1585. The electronic coupling between different molecules and nano-particles, as well as the charge transport through the particles and the interfaces will be studied using the real-time propagation approach to time-dependent density functional theory. By simulating electron dynamics in real-time and on a real-space grid, the project aims at unravelling charge-transport pathways in complex, interface-governed materials. Effort will also be devoted to use and develop exchange-correlation approximations that combine computational efficiency with a reasonable accuracy for long-range charge-transfer.
Scientific field: 327-01 Electronic Structure, Dynamics, Simulation
University: Universität Bayreuth
Target system: parallel computer Fritz
Engineering Sciences
Mechanics and Constructive Mechanical Engineering
Bio-FROSch: Modeling and simulation of pharmaco-mechanical FSI for an enhanced treatment of cardiovascular diseases and non-Newtonian micro-macro blood flow simulations (07/2023–06/2024)
StroemungsRaum: Neuartige Exascale-Architekturen mit heterogenen Hardwarekomponenten für Strömungssimulationen (03/2023–09/2025)
FLINSENOI: Flow induce self-noise (01/2023–12/2023; LARGE SCALE)
In this project, numerical studies are carried out to investigate the influence of surface modifications on the near-wall turbulence of external and internal wall-bounded flows. The surface modifications consist of streamwise aligned grooves in combination with regions of strong transverse curvature between the grooves. The aim of the surface modifications is to reduce the near-wall turbulent fluctuations that can lead to flow-induced vibrations of the adjacent structure and/or to flow-induced sound which is undesired in many applications. Recent investigations show that such surface modifications could lead to a 20 % decrease in the average wall shear stress and result in local reductions in the turbulent intensities, Reynolds stress, the temporal velocity spectrum, and the turbulent dissipation rate. The analysis within the anisotropy-invariant space revealed a local tendency towards flow relaminarization. Improvements of the surface modifications should be investigated in a numerical study using the finite volume code OpenFOAM. The size of the improved surface modifications are in the order of the viscous sublayer. Transient and scale-resolving simulations are needed to extraxt the quantities of interest, such as turbulent dissipation, anisotropy of turbulence, Reynolds stress, flow-acoustic source terms, velocity and pressure fluctuations as well as temporal spectra. Therefore a high-resolution mesh in combination with multi-node parallelized simulations are required.
Scientific field: 404-03 Fluid Mechanics; 402-04 Acoustics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
FRASCAL-FE: Computational continuum mechanics simulations at LTM for FRASCAL (11/2022–11/2023)
FRASCAL improves understanding of fracture in complex heterogeneous materials by developing simulation methods able to capture the multiscale nature of failure. With i) its rooting in different scientific disciplines, ii) its focus on the influence of heterogeneities on fracture at different length and time scales as well as iii) its integration of highly specialized approaches into a “holistic” concept, FRASCAL addresses a truly challenging cross-sectional topic in mechanics of materials.
Specifically, project P8 (“Fracture in Polymer Composites: Meso to Macro”) aims to study the influence of different mesoscopic parameters, including micro-structure morphology, on the macroscopic fracture properties of nano-particle reinforced polymers.
Scientific field: 402-2 Mechanics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
AkuRad: Fluid-Structure-Acoustic Interaction of Enclosed Radial Fans (09/2022–08/2025)
The research project comprises the acoustics of a radial fan operated in a spiral housing. Radial fans have a wide range of applications in a wide variety of fluid engineering applications, in which the requirements for sound radiation are becoming increasingly dominant.
The goal of the present application is to investigate the multiphysical interrelationships of flow-related sound radiation of radial fans in volute casings using a combined, experimental, simulation-based approach. It is important to treat the excitation paths for the casing structure separately: There are the fluid dynamic pressure fluctuations as well as the flow induced acoustic pressure field within the casing.
The numerical simulation methods are verified and validated against experimentally obtained data. Subsequently, developed methods are used to investigate the sound generation in the fan impeller and the resulting sound radiation through the spiral housing. This is done by means of variant studies. The variants are formed by different inflow conditions to the radial impeller, which influence the flow-induced sound generation in the impeller and thus also the casing excitation and sound radiation into the far field.
The innovation of the research work results from the fact that for the first time it is possible to develop a simulation method for enclosed rotating systems, which maps the entire chain of fluid-structure-acoustic interaction. In a complementary approach with experimental investigations, a conscious separation of different mechanisms in sound generation and their complex interactions results in knowledge with a high general validity that is not yet available in literature for enclosed radial fans. The findings allow conclusions to be drawn as to which part of the machine (impeller flow, casing structure, etc.) changes the radiated sound with which influencing parameters. In the development process, this results in a specific treatment of the acoustic problem areas.
The basic-oriented investigations have a high general validity and form the basis of future design rules for noise-reduced radial fans. The coupled simulation method provides a calculation tool that allows the flow- induced sound generation and its propagation in radial impellers to be analyzed in the frequency and time domain.
Future work will focus on the use of optimization algorithms for noise reduction, which consider not only the inflow conditions to the impeller and their interaction with the leading edge of the blade, but also the material parameters and the shape of the casing structure.
Scientific field: 404-03 Fluid Mechanics; 404-04 Hydraulic and Turbo Engines and Piston Engines; 402-04 Acoustics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
FRASCAL-MD: Particle-based computing at LTM for FRASCAL (09/2022–09/2024)
FRASCAL improves understanding of fracture in brittle heterogeneous materials by developing simulation methods able to capture the multiscale nature of failure. With i) its rooting in different scientific disciplines, ii) its focus on the influence of heterogeneities on fracture at different length and time scales as well as iii) its integration of highly specialized approaches into a “holistic” concept, FRASCAL addresses a truly challenging cross-sectional topic in mechanics of materials.
In particular, sub-project P6 (“Fracture in Thermoplastic Polymers: Discrete-to-Continuum Coupling”) provides a link between the level of atoms and the continuum with specific interest in the multiscale modelling and simulation of polymer fracture.
Scientific field: 402 Mechanics and Constructive Mechanical Engineering
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz & GPGPU cluster Alex
Process Engineering, Technical Chemistry
CRC1411D04: Design of Particulate Products: Modelling particle aggregation and assembly into optimal structures (03/2023–02/2024)
Fluid Mechanics, Technical Thermodynamics and Thermal Energy Engineering
AkuRad: Fluid-Structure-Acoustic Interaction of Enclosed Radial Fans (09/2022–08/2025)
FLINSENOI: Flow induce self-noise (01/2023–12/2023; LARGE SCALE)
EnSimTurb: Towards ensemble simulations of fully developed turbulence (12/2022–04/2026)
AOTTP-DFG18-1: Characterization of molecular diffusion in electrolyte systems (07/2022–07/2024)
Electrolytes conduct electric current by the movement of ions while blocking the free movement of electrons. For applications of electrolytes as working fluids, the transport of ions is important and can be subdivided into diffusion, convection, and, in the presence of an electric field, migration. The diffusion process has shown to be of special interest since it is a limiting factor for the performance of applications where large concentration gradients can build up when electro-active ions react at electrode surfaces. Therefore, exact knowledge of molecular diffusion coefficients is required for the design and optimization of efficient processes involving electrolytes.
Scientific field: 404-01 Energy Process Engineering; 404-02 Technical Thermodynamics
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz & GPGPU cluster Alex
Materials Science
ASiMENiS: Atomistic simulation of microstructure elements in Ni-based superalloys (01/2023–12/2023)
Single-crystal Ni-based superalloys play a key role for the efficiency of gas turbines for aviation and power generation. The optimization of these high-performance structural materials requires an understanding of the influence of the elements of the microstructure on the mechanical properties. The goal of this project is to understand dislocation mobility and the formation of local defect phases in the microstructure. The atomistic simulations involve high-throughput ab-initio calculations and large-scale molecular-dynamic simulations with machine-learning potentials based on the atomic cluster expansion.
Scientific field: 406 Materials Science
University: Ruhr-Universität Bochum
Target system: parallel computer Fritz & GPGPU cluster Alex
Systems Engineering
Voxray: Quantenrekonstruktion für industrielle Computertomographie (07/2023–06/2024)
Innerhalb der zerstörungsfreien Werkstoffprüfung ist die Computertomographie (CT) der Goldstandard, um Bauteile schnell und präzise zu vermessen und auf Fehler zu analysieren. Ein typischer Inspektionsablauf ist in der folgenden Abbildung dargestellt. Die bislang etablierte Rekonstruktions-Software kämpft regelmäßig mit Problemen, bei denen das 3D- Modell nicht mit dem realen Objekt übereinstimmt. Diese treten besonders bei Verbundmaterialien aus Kunststoff und Metall auf und machen die Qualitätssicherung schwierig und zeitaufwendig. Zugleich scheitert bestehende Software an Spezialverfahren wie Roboter-CT oder der sogenannten Laminographie, die beispielsweise in der immer wichtiger werdenden Batteriezellenentwicklung angewandt wird. Wir entwickeln aktuell die neuartige Quanten-Rekonstruktionstechnik (QRT), die den Rekonstruktionsschritt in der Industrie-CT revolutioniert. Mit dieser neuen Technik wird die Ausbreitung der einzelnen Röntgenphotonen (der Quanten) simuliert und anschließend invertiert. Mithilfe eines iterativen Optimierungsalgorithmus wird so eine Rekonstruktion des Objektes erstellt.
Scientific field: 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation; 407-02 Measurement Systems
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
GastroDigitalShirt: Development and test of deep neural network models for the automatic detection of body sounds to monitor digestion in control group and patients with intestinal disorders (09/2022–11/2023)
Electrical Engineering and Information Technology
MMSP: Multi-Microphone Speaker Separation (07/2023–06/2024)
In a scenario with multiple active sound sources, noise, and reverberation, the task of extracting and separating sources is known to be challenging. Using several spatially distributed microphones, the task can be formulated as a multiple-input multiple-output problem. Our research focuses on separating speech sources in predefined spatial regions, for example, related to the seats in a car or the seats in a conference room. We employ deep neural networks for the separation process.
Scientific field: 408 Electrical Engineering and Information Technology
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: parallel computer Fritz
Computer Science
MIA-NORMAL: Medical Image Analysis with Normative Machine Learning (10/2023–09/2028; LARGE SCALE)
MAGNET4Cardiac7T: Physics-Informed Deep Learning Algorithms for Modeling Electro-Magnetic Fields in the Human Thorax (10/2023–09/2024)
A major obstacle to the widespread use of ultrahigh-field magnetic resonance imaging (UHF-MRT) is the complex distribution of electromagnetic waves in the patient’s thorax, which has a very negative impact on image quality and also carries the risk of undesirable overheating of the tissue. Conventional computational methods for calculating the 3D field distribution require several days and thus cannot be used on patients. Therefore, a new approach is developed in the present research project: Physics Informed Neural Networks (PINN) will be used to train neural networks based on Maxwell’s equations, which will allow to calculate the energy deposition in the body within a few minutes. Together with the German Center for Heart Failure, PINNs, which have so far only been described for two-dimensional applications, will be further developed for the three-dimensional case.
Scientific field: 409 Computer Science
University: Julius-Maximilians-Universität Würzburg
Target system: GPGPU cluster Alex
TriFORCE: Learning adaptive reusable skills for intelligent autonomous agents (07/2023–06/2024)
This project will extend the current limits of Deep RL to improve its practicality in realistic high- dimensional problems. Our contributions will be a step forward in unleashing the full potential of Deep RL by making it more efficient and easier to use in real-world environments. We will achieve our goal by addressing the following fundamental problems of Deep RL: Computational and data requirements, human supervision and tuning, and task overfitting. Although agents trained with Deep RL perform excellently, they have a strong tendency to overfit the available data.
In this project, we will show that these seemingly different problems have similarities and common causes, and we will address them by introducing a new unifying view that will guide our research for more efficient and practical Deep RL methods that can be used in everyday applications.
We will demonstrate the critical importance of our methodological advances in learning locomotion skills in a leg-operated wheelchair. In this application, the wheelchair needs to move robustly and safely at different speeds, avoid obstacles and adapt to different terrains. In addition, the gait pattern should be
adapted to the needs and preferences of the human user. We will demonstrate the importance of our algorithmic solutions in a realistic problem where multiple skills are required, such as navigating to a destination, walking up stairs and avoiding obstacles along the way.
The TriFORCE team is made up of outstanding scientists, an advisory board with renowned researchers, female leaders from academia and partners from industry.
Scientific field: 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation
University: Julius-Maximilians-Universität Würzburg
Target system: GPGPU cluster Alex
Voxray: Quantenrekonstruktion für industrielle Computertomographie (07/2023–06/2024)
XXL-CT-Segmentation: Instance segmentation of XXL-CT Volumes (06/2023–06/2024)
XXL-Computed Tomography (XXL-CT) is able to produce large scale volume datasets up to and above 10,0003 voxels which can relate up to many terabytes in file size and can contain multiple 10,000 of different entities of depicted objects with varying properties (shapes, densities, materials, compositions) which are hard to delineate. This project aims to develop deep learning based instance segmentation algorithms to delineate these entities for the data exploration process.
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
EmpkinSA01: Multimodal Bodyshell Camera (05/2023–10/2025)
Sub-project A01 of EmpkinS explores a novel, multimodal sensor concept for high-precision, non-contact detection of the envelope of the human body and the velocity vector of each point on this envelope. For this purpose, a micro-doppler aperture synthesis radar is combined with an optical depth camera and the strengths of the sensors are combined. The body envelope and its movements are detected with outstanding precision and at a high measuring rate. With these properties, the system is a central basis for the research programme of the CRC EmpkinS. To be able to detect a human body and capture its pose in the radar and the depth camera, a large variety computer vision algorithms is required, ranging from simple Region-of-Interest detection of the human in an image, to segmentation, 3D reconstruction, and human pose estimation algorithms. In terms of quality, Deep Learning approaches have proven outstanding performance with respect to traditional optimization-based approaches. Therefore, this project aims at leveraging Deep Learning approaches, which operate on the previously-mentioned sensor data such as RGB ((depth-)camera), RGB-D (depth-camera) and radar data.
Scientific field: 409 Computer Science
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
digiOnko: digiOnko – Mit digitaler Medizin gegen Brustkrebs, AP5 - Histopathologie (12/2022–09/2024)
The digiOnko project (https://www.digionko-bayern.de/) aims at improving the screening, early detection, diagnosis, treatment and aftercare for women with breast cancer. One aspect of this project is the use of machine learning methods in digital pathology. Deep learning methods will be used to analyze digital Whole Slide Images (WSI), in particular to automatically calculate scores and improve treatment decisions.
Scientific field: 409-05 Materials Engineering
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
DeepPano: Erzeugung von Panoramabildern aus 3D-Laser-Punktewolken und Kamerabildern (12/2022–12/2023)
Today, it is possible to capture the 3D-Geometry of real-world objects and rooms in high quality by using modern sensors and algorithms for 3D reconstruction. These virtual models open the door for many applications, e.g., floor plans can be generated, and distances measured virtually. However, for virtual walkthroughs, the resulting models are usually not sufficient yet to achieve high-quality renderings from arbitrary views. With 360°-photographs (panoramas) it possible that a user explores a scene in photo- realistic quality, but only by looking around, and not moving freely. Image-based rendering is a well- researched approach to lift this restriction: photographs of the scene are reprojected to novel views, based on a coarse 3D model of the world. Yet, image-based rendering often suffers from artifacts, and most often does not deliver photo-realistic quality.
In the project we research novel approaches based on deep neural networks that improve the quality of the images generated by image-based rendering and that can remove or reduce typical artifacts. Focus of the project is in the first phase the generation of panoramas from a set of nearby inside-out take images that exploit such techniques to generate artifact-free panoramas from few input images. Later on, we will focus on approaches that allow the free movement of a user in real-time, making it possible to walk through a scene virtually.
The work in the project is centered around data obtained by the 3D-indoor-scanning systems of the industrial partner NavVis (Munich), who can deliver high-quality 3D point clouds and registered input images. We will thus focus on novel neural rendering techniques that as point clouds as proxy geometry, on the generation of panoramic images, and the research of possibilities to achieve free-viewpoint video, based on this data, in real-time.
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
PatRo-MRI-2: Pathology-robust image reconstruction in Magnetic Resonance Imaging (10/2022–12/2026)
HEISSRISSE: Massively Parallel Simulation of the Melt Pool Area during Laser Beam Welding using the Lattice Boltzmann Method (10/2022–11/2023)
HEISSRISSE: As a flexible and contact-free joining technology, laser beam welding has increasingly gained importance. Processing of alloys with large melting range poses a challenge due to their solidification cracking tendency. Solidification cracks form due to critical stress and strain states of the dendritic microstructure with interdendritic melt. Despite the high industrial relevance, there are only approaches addressing single aspects of the problem, metallurgically or structurally oriented. The research unit “Solidification Cracking during Laser Beam Welding – High Performance Computing for High Performance Processes” aims at developing quantitative process understanding of the mechanisms of solidification cracking and their relation to process parameters. The sub-project aims at simulating the dynamics of the melt pool with a resolution of about one micron. This is achieved using a numerical model consisting of approximately 109 computational cells that have to be computed for more than 105 time steps. The simulation will model the phase change at melting and solidification, the energy input from the laser, the expansion and contraction, and the heat and mass transport in the melt pool. Simulations of such complexity require the computational power of parallel high-performance systems and can only be realized using optimized parallel algorithms and modern software technologies. In this sub-project, the lattice Boltzmann method (LBM) will be used and extended to correctly capture the various physical effects in the melt pool. Compared to other numerical schemes, the LBM is well-suited for parallel computing and modern computer architectures that include hardware accelerators. The implementation will be based on the HPC framework waLBerla specifically designed for implementing complex multi- physics applications. Using abstraction layers and code generation concepts, software developed with waLBerla is sustainable, i.e., portable to future computer architectures. One of the significant aspects of this project lies in the validation of newly implemented models and algorithms, and in the interoperability with models from the partners in the research unit. This will be achieved via the close cooperation with neighboring sub-projects.
Scientific field: 409-08 Massively Parallel and Data-Intensive Systems
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
GastroDigitalShirt: Development and test of deep neural network models for the automatic detection of body sounds to monitor digestion in control group and patients with intestinal disorders (09/2022–11/2023)
IRRW: Scaling Inverse Rendering to the Real World (08/2022–07/2025)
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
ICETHICKNESS: Machine learning-based retrieval of ice thickness / internal structures from radargrams (07/2022–06/2023)
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
InTimeVRSimulPatMod: In-time Virtual Reality Simulation Patient Models: Machine Learning and immersive-interactive Modeling of Virtual Patient Bodies (05/2022–05/2025)
Completed NHR compute time projects on Alex and Fritz at NHR@FAU
Humanities and Social Sciences
Humanities
ProtCTRL: A conditional transformer for à la carte protein sequence generation (07/2022–07/2023)
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
Life Sciences
Biology
ImmunoDomains: Interplay of immune receptors and lipid environment in signaling (10/2022–09/2023; LARGE SCALE)
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
ProtCTRL: A conditional transformer for à la carte protein sequence generation (07/2022–07/2023)
DNARepairTDG - DNA Repair by Thymine DNA Glycosylase (06/2022–09/2023)
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
Resolving the Structure of mRNA-Vaccine Lipid Nanoparticles (Alex early access, Q4/2021–Q1/2022)
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
A deep unsupervised Model for Protein Design (Alex early access, Q4/2021–Q1/2022)
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
Dynamics of B2AR-Gs(GTP) (Fritz early access, Q1/2022)
Scientific field: 201-02 Biophysics
University: Leipzig University
Natural Sciences
Chemistry
Ion-catch: Molecular Modelling based design of ligand shells to functionalize magnetic nanoparticles for the removal of heavy metal pollutants from water (08/2022–06/2023)
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
CatalAcetylen: Acetylene selective hydrogenation to ethylene catalyzed by bi- and trimetallic alloys: AI search for new catalysts (07/2022–12/2022)
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
Physics
AHPO4TauID: Automatic hyperparameter optimization of Graph Neural Networks for tau neutrino identification in KM3NeT/ORCA (05/2023–07/2023)
KM3NeT/ORCA is a neutrino telescope currently under construction in the Mediterranean deep sea. It detects neutrinos of all flavors produced in the atmosphere indirectly by measuring the Cherenkov light emitted when fast charged particles are produced during neutrino-nucleon interactions. One of the scientific goals of the KM3NeT Collaboration is to further constrain the least-well-known elements of the three-flavor leptonic mixing matrix. To this end, we will in particular seek to identify tau neutrino interactions in the detector volume, a task known as tau neutrino event identification. A new approach to this problem is studied in this project, where state-of-the-art Graph Neural Networks are trained to directly identify tau neutrino events in the detector data. The promising results achieved in first trainings will be further improved in this NHR project by conducting a systematic search for the optimal hyperparameters of the networks, a task known as automatic hyperparameter optimization.
Scientific field: 309-01 Nuclear and Elementary Particle Physics, Quantum Mechanics, Relativity, Fields
University: Friedrich-Alexander-Universität Erlangen-Nürnberg
Target system: GPGPU cluster Alex
CLSfiniteV: Finite volume study of 2 + 1f QCD from lattice simulations (10/2022–09/2023; LARGE SCALE)
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
CatalAcetylen: Acetylene selective hydrogenation to ethylene catalyzed by bi- and trimetallic alloys: AI search for new catalysts (07/2022–12/2022)
Functional renormalization group calculations (Fritz early access, Q1/2022)
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: RWTH Aachen
Massively parallel simulation via Markov Chain Monte Carlo techniques (Fritz early access, Q1/2022)
Scientific field: 307-02 Theoretical Condensed Matter Physics
University: Julius-Maximilians-Universität Würzburg
Engineering Sciences
Thermal Engineering/ Process Engineering
Flow around a Wind Turbine Blade at Reynolds Number 1 Million (Fritz early access, Q1/2022)
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
Evolution of drops in homogeneous isotropic turbulence (Alex early access, Q1/2022)
Scientific field: 404-03 Fluid mechanics
University: University of Bremen
Materials Science and Engineering
Materials4.0 - AITDB: Ab initio thermodynamic database development (10/2022–09/2023)
Phase diagrams have revolutionized materials development by providing the conditions for phase stabilities and transformations, and thereby a thorough thermodynamic understanding of materials design. However, the majority of today’s phase diagrams are based on scarce experimental input and often rely on daring extrapolations. Every multicomponent phase diagram relies on a fragile set of phase stabilities as very recent studies show.
Within this project, materials design is raised to the next level by providing a highly accurate first principles thermodynamic database. First principles, alias ab initio, approaches do not require any experimental input and can operate where no experiment is able to reach. However, they have been limited to zero-Kelvin or low-temperature approximations which are not representative of phase diagrams.
Utilizing our unique expertise in high-accuracy finite-temperature ab initio simulations, we have developed an extremely efficient procedure including novel methods accelerated by machine learning potentials. This procedure facilitates a highly efficient determination of Gibbs free energies, thereby allowing convergence also of properties depending on the second-order derivatives, such as the heat capacity and thermal expansion.
With these tools, we have now reached a stage where a large ab initio thermodynamic database will be computed for elements across the periodic table. The main focus will be on phase stabilities of various phases, including dynamically unstable ones. Having acquired such a database, the phase stabilities can be put into practice by re-parametrizing binary phase diagrams and studying the implications on multicomponent phase diagrams.
Scientific field: 406 Materials Science
University: Universität Stuttgart
Target system: parallel computer Fritz