NHR Compute Time Projects

Abstract

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

Principal Investigator: Prof. Dr. Dominik Munz, Saarland University

Target system: parallel computer Fritz

Project Report (12/2022)

Our project deals with molecules with unusual electronic structures. We aim to harness these electronic structures in green catalysis as well as for material design in organic electronics such as photovoltaics. For bringing these molecules to application, a profound understanding of their complicated electronic structures is required.

As such, we perform synthetic, computational- and spectroscopic work hand-in-hand for optimal synergy. So far, we have focused on late transition metal complexes for catalysis and carbene-derived organic materials. A recent breakthrough was the isolation of the first example of a terminal nitrene of palladium.

Another landmark finding was, how carbenes stabilize radicals for organic electronics. In subsequent steps, we wish to extend our studies to other earth-abundant elements for enhanced sustainability.

Abstract

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 fields: 301 Molecular Chemistry; 302-03 Theory and Modelling

Principal Investigator: Dr. Kristyna Pluhackova, University of Stuttgart

Target system: parallel computer Fritz & GPGPU cluster Alex

Publications

Abstract

Scientific field: 302 Chemical Solid State and Surface Research

Principal Investigator: Prof. Dr. Matteo Bianchini, Universität Bayreuth

Target system: parallel computer Fritz

Abstract

Ferroelectrics are insulators with a spontaneous electrical polarization that can be reversed by an external electric field. Their properties make them sought-after functional materials with a wide range of potential applications. A basic requirement for the occurrence of ferroelectricity is that the material crystallizes in a polar space group.

In this work we want to predict new ferroelectric metal fluorides. To this end, we will compile an overview of polar metal fluorides, which we will investigate using crystallographic and quantum chemical methods. The starting point is a database screening that yields fluorides in 105 structure types that potentially crystallize in polar space groups. We will perform DFT calculations on individual representatives of the structure types to confirm their polar structure.

We will perform structural optimizations and calculate vibrational frequencies at the Γ-point in the harmonic approximation to verify that our candidates are dynamically stable at 0 K. If the DFT structural optimization converges to a nonpolar structure, we will classify the structural model of this compound as potentially erroneous and suggest a redetermination of the crystal structure. We will perform the DFT calculations with the program CRYSTAL23 using the hybrid functional PBE0 and atom-centered basis functions. We will adopt technical parameters and convergence criteria from our previous theoretical studies on fluorides. All structure types of our screening have less than 200 atoms per unit cell. From our preliminary investigations and the system sizes, we extrapolate the required computing time to about 500,000 CPU hours. For all structural optimizations, we estimate the requirement at 100,000 CPU hours, for the frequency calculations at 400,000 CPU hours. Crystal23 is effectively parallelized via OpenMP and MPI. For our system sizes, we use 16 to 64 cores and 4 GB to 32 GB RAM as standard.

Scientific field: 302 Chemical Solid State and Surface Research

Principal Investigator: Prof. Dr. Florian Kraus, Philipps-Universität Marburg

Target system: parallel computer Fritz

Abstract

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

Principal Investigator: Prof. Dr. Andreas Görling, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz

Project Report (10/2023)

Our calculations focus on the atom-resolved understanding of novel catalyst systems, namely catalysis on liquid interfaces (CLINT). In contrast to usual heterogeneous catalysis taking place on metal surfaces, the novel systems have the advantage of better control of the catalytic processes and being less prone to unwanted side-effects like poisoning and coking. Two different catalytic concepts are mainly studied: Supported Catalytically Active Liquid Metal Solutions (SCALMS) and Surface Catalysis with Ionic Liquid Layers (SCILL). Within SCALMS, active atoms like Pt are distributed into a liquid solvent metal like Ga. The Pt atoms move around in the liquid and occasionally reach the surface, where they are available to the catalysis. Our simulations focus on how this motion actually takes place on an atomistic level. Since liquid metals are studied rarely by theory on larger scales, we set up novel machine learning force fields (ML-FF) for a cheap but nevertheless accurate simulation of them, enabling averaging of the surface properties. We further consider in our ML-FFs the interaction of the metal solvents with the support material, on which droplets of them are placed in reality and the formation of oxide layers on the surface and intermetallic phases in the liquid, thus being able to study a large part of the real-world complexity of SCALMS. Electronic properties of the systems can be calculated by periodic DFT, using snapshots of the ML-FF simulations. Within SCILL, the catalytically active metal surface is coated with a thin ionic liquid (IL) film. Depending on the IL species present, the interactions with the reactive species can be fine-tuned, further, unwanted side-reactions are suppressed by the coating. Our simulations aim at a detailed description of the interactions of IL molecules with the metal surface. We optimize IL adsorption patterns by DFT and simulate scanning tunneling microscope (STM) images of them, we further study the electronic properties of the IL/metal interface by applying electric fields to model systems with CO molecules adsorbed on the surface, acting as probing species. In close collaboration with the Zahn group, we are able to link the Coulomb interactions of explicit IL molecules obtained from traditional force field simulations to effective applied electric fields in model DFT surfaces. Besides these two main branches, we use our calculation time for several smaller projects, all covering novel surface systems, like the interaction of hexagonal bornitride (h-BN) sheats on a Rh surface with Br molecules. Due to fascinating nanomesh (moire) patterns arising from a unit cell mismatch of the Rh surface and the h-BN sheet, the interaction with gaseous Br is versatile and complicated. Besides thorough DFT calculations, we are able to simulate the real-time dynamics of such a system using a ML-FF trained for that purpose.

Abstract

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 fields: 303-02 Theoretical Chemistry; 406 Materials Science

Principal Investigator: Prof. Dr. Dirk Zahn, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target systems: parallel computer Fritz & GPGPU cluster Alex

Abstract

The quantum world fundamentally differs from the classical world, that we intuitively understand through our daily experience. The mathematical description of quantum systems with many degrees of freedom suffers from a curse of dimensionality and predictions of physical theories are fundamentally restricted to be probabilistic. While these properties form the basis for the potential utility of quantum computers, they are also the cause of the greatest obstacles when creating such devices. In our Helmholtz Young Investigator Group, we aim to exploit the known strengths of machine learning approaches in dimensional reduction and pattern recognition for the advancement of near-term quantum technology. In particular, we will push the boundaries of classical simulations for the certification of quantum simulators, which will become a valuable extension of our scientific toolbox to study complex quantum systems. The basis of this research is a representation of many-body quantum states as artificial neural networks. The goal of this project is on the one hand to improve our understanding of this novel approach, and, on the other hand, to exploit the new capabilities in order to investigate otherwise inaccessible terrain in the realm of correlated matter.

Scientific field: 307-02 Theoretical Condensed Matter Physics

Principal Investigator: Dr. Markus Schmitt, University of Regensburg

Target system: GPGPU cluster Alex

Abstract

The possibility to precisely manipulate and control artificial quantum systems constitutes the foundation of emerging quantum technologies. Of particular interest for the field of correlated quantum matter is the possibility to emulate theoretical models using quantum mechanical degrees of freedom. However, our understanding of how to manipulate such systems using external drives is in its infancy, especially in the presence of strong interactions. We propose to overcome some of the current limitations by combining techniques from machine learning with ideas from quantum many-body physics. Within this project, we will develop simulation methods for periodically driven systems based on neural quantum states (NQS), and a new interpretable framework for tensor-networks- based reinforcement learning (RL) to optimize control of quantum matter.

Scientific field: 307-02 Theoretical Condensed Matter Physics

Principal Investigator: Dr. Markus Schmitt, University of Regensburg

Target system: GPGPU cluster Alex

Abstract

The urgent need to decarbonise our society leads to major materials-science challenges in the transition from fossil to hydrogen-based fuels. It is becoming increasingly critical that H is integrated into materials design principles and in lifetime predictions of engineering parts. This calls for a thorough understanding of the interplay between H atoms and the local geometric and chemical details of the microstructure. This project addresses the impact of H on single-crystal Ni-based superalloys and on grain boundaries in ferritic alloys. The atomistic simulations involve high-throughput ab-initio calculations and the development of machine-learning potentials based on the atomic cluster expansion for large-scale atomistic simulations.

Scientific fields: 307-02 Theoretical Condensed Matter Physics; 406-03 Microstructural Mechanical Properties of Materials

Principal Investigator: PD Dr. habil. Thomas Hammerschmidt, Ruhr-Universität Bochum

Target system: parallel computer Fritz

Abstract

Molten salts electrolysis is a well-known technique to extract metals from ores. Nevertheless, the optimization of cell design and associated processes are limited by incomplete thermodynamic databases. When available, the data can be employed to devise corrosion phase diagrams that are crucial to draw next generation electrochemical reactors. The key quantity in such diagrams is the formation energy of the metal ions to extract and that would dissolve in the electrolyte, but it’s evaluation from experiments is challenging and expensive. An alternative solution is to calculate such quantities by first-principles simulations. While the latter are still challenging, the computational effort is eased by leveraging advanced computational techniques such as active learning density-functional theory molecular dynamics, machine learning neural networks, and reproducible high-throughput calculations. In particular, we focus on the computation and prediction of ion formation energies in molten chloride electrolytes, such as LiCl, KCl, NaCl, CaCl₂, and MgCl₂. This work founds the base to enable selective metal extraction by providing theoretical guidance that complements experimental efforts.

Scientific fields: 307 Condensed Matter Physics; 406-01 Thermodynamics and Kinetics of Materials

Principal Investigator: Prof. Dr.-Ing. Lucio Colombi Ciacchi, Universität Bremen

Target system: GPGPU cluster Alex

Abstract

We will explore in a spatially and time-resolved manner charge carrier dynamics on three-dimensional TI surfaces subject to time-dependent laser pulses or radiative driving. We will in particular explore the high-harmonic generation for pulse-type of applied external fields, a timely subject of much conceptual, experimental and technological interest. To this end we will employ  complementary quantum mechanical wave packet-based techniques and semiclassical path integral methods, with emphasis on reaching the regimes of parameters where the well-established so-called three-step-method may break down and non-classical mechanisms are responsible for the emission process.

Scientific field: 307-02 Theoretical Condensed Matter Physics

Principal Investigator: apl. Prof. Dr. Juan Diego Urbina, University of Regensburg

Target system: GPGPU cluster Alex

Project Report (07/2024)

This test/porting project was used to determine the viability of parameter exploration on GPUs using in-house software on the NHR GPU cluster Alex. We were able to show a perfectly linear scaling of runtime with problem size and establish that the runtime is roughly an order of magnitude faster on an A100 than on a typical 64-core CPU node.

Furthermore, we began a two-dimensional parameter space exploration of a massive Dirac model driven by a linearly polarized laser pulse. These time-resolved simulations can be used e.g. to calculate high-harmonic spectra. It was not possible to obtain converged results within the computation time of the test/porting project, but it allowed an estimation of the runtime required for the follow-up project.

Abstract

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

Principal Investigator: Dr. Andreas Buchheit, Saarland University

Target system: parallel computer Fritz

Publications

Abstract

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

Principal Investigator: Prof. Dr. Janina Maultzsch, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz

Publications

S. Wolff, R. Gillen, and J. Maultzsch: Electronic Properties and Interlayer Interactions in Antimony Oxide Homo- and Heterobilayers. Phys. Status Solidi B 260, 2023. DOI: 10.1002/pssb.20

2300376.

Abstract

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

Principal Investigator: Prof. Dr. Kai Phillip Schmidt, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz

Abstract

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

Principal Investigator: Dr. Jan Wilhelm, University of Regensburg

Target system: parallel computer Fritz

Abstract

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

Principal Investigator: Dr. Lorenzo Crippa, Julius-Maximilians-Universität Würzburg

Target system: parallel computer Fritz

Project Report (03/2024)

Abstract

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

Principal Investigator: Prof. Dr. Markus Heyl, Augsburg University

Target system: GPGPU cluster Alex

Project Report (09/2024)

In this project, we focus on the neural quantum state method which is a compressed representation of quantum many-body systems. Previously, the neural networks used in this method are usually shallow with only around 10 layers and thounsands of parameters due to the difficulty of optimization. We develop a new optimization method named MinSR that allows us to implement deep neural quantum states with up to 64 layers and 1 million parameters, which are significantly larger than previous neural quantum state practices. This allows us to obtain higher variational accuracy in several quantum systems and more reliable properties of the underlying quantum spin liquids. The deep neural quantum state with MinSR will open a new route for the numerical studies of strongly correlated many-body quantum systems. Apart from the current applications on quantum spin liquids, it can also be applied to fermionic systems or quantum dynamics for a better understanding on these systems.

Project Report (07/2023)

In this project, we utilize so-called neural quantum state method, which provide a compressed representation of the quantum many-body state into artificial neural networks. Up to now this method has been limited shallow networks with only around thousands of parameters, which has been caused by the computational complexity of optimization.

We have developed a new optimization method named MinSR that allows us to train deep neural quantum states of unprecedented size with up to 64 layers and 1 million parameters. This has allowed us to significantly improve the variational accuracy of the ground state for several paradigmatic quantum systems.

We expect that the deep neural quantum state trainable with MinSR will open up a new route for the numerical studies of strongly correlated many-body quantum systems. Apart from the current applications on quantum spin liquids, it can also be applied to fermionic systems or quantum dynamics in the future.

Abstract

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

Principal Investigator: Prof. Dr. Carsten Honerkamp, RWTH Aachen University

Target system: parallel computer Fritz

Project Report (01/2024)

Abstract

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

Principal Investigator: Prof. Dr. Fakher Assaad, Julius-Maximilians-Universität Würzburg

Target system: parallel computer Fritz

Project Report (10/2023)

The ALF library implementation of the auxiliary field quantum Monte Carlo method allows for simulations of lattice fermions. Using this approach we have investigated emergent and topological phenomena in quantum matter. Highlights include a novel formulation for the simulation of the canonical Su-Schrieffer-Heeger model of lattice vibrations coupled to electrons on a square lattice. This model shows emergent Dirac fermions as well as Landau forbidden continuous transitions between different broken symmetry states. We have equally pursued our work in the domain of magnetic impurities in metals. By adopting a setup where there is a dimensional mismatch between the magnetic impurities and conduction electrons we can realize metallic magnetic phase transitions and analyze the critical behavior.

Publications

B. Frank, Z. H. Liu, F. F. Assaad, M. Vojta, and L. Janssen: Marginal Fermi liquid at magnetic quantum criticality from dimensional confinement. Phys. Rev. B 108, 2023. DOI: 10.1103/PhysRevB.108.L100405.

J. Schwab, F. P. Toldin, and F. F. Assaad: Phase diagram of the SU(N) antiferromagnet of spin S on a square lattice. Phys. Rev. B 108, 2023. DOI: 10.1103/PhysRevB.108.115151.

Z. H. Liu, Y. D. Liao, G. Pan, M. Song, J. Zhao, W. Jiang, C.-M. Jian, Y.-Z. You, F. F. Assaad, Z. Y. Meng, and C. Xu: Disorder Operator and Rényi Entanglement Entropy of Symmetric Mass Generation. Phys. Rev. Lett. 132, 2024. DOI: 10.1103/PhysRevLett.132.156503.

A. Götz, M. Hohenadler, and F. F. Assaad: Phases and Exotic Phase Transitions of a Two-Dimensional Su-Schrieffer-Heeger Model. Phys. Rev. B 109, 2024. DOI: 10.1103/PhysRevB.109.195154.

Abstract

Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the
ground-breaking Hanbury Brown-Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius by half. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

Scientific field: 308 Optics, Quantum Optics and Physics of Atoms, Molecules and Plasmas

Principal Investigator: Dr. Marco Knipfer, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Abstract

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

Principal Investigator: Prof. Dr. Gerhard Wellein, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz & GPGPU cluster Alex

Abstract

Scientific field: 309 Particles, Nuclei and Fields

Principal Investigator: Prof. Dr. Andreas Schäfer, University of Regensburg

Target system: parallel computer Fritz

Abstract

Scientific fields: 309 Particles, Nuclei and Fields; 311 Astrophysics and Astronomy

Principal Investigator: Dr. Jonas Glombitza, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Abstract

Scientific field: 309 Particles, Nuclei and Fields

Principal Investigator: PD Dr. Wolfgang Söldner, University of Regensburg

Target system: parallel computer Fritz

Project Report (01/2024)

Abstract

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

Principal Investigator: Prof. Dr. Andreas Schäfer, University of Regensburg

Target system: parallel computer Fritz

Project Report (03/2024)

The Large Hadron Collider at CERN, Geneva has so far failed to find “Beyond the Standard Model” physics (BSM). Therefore, its luminosity (basically its collision rate) is in the process of being much increased, to substantially enhance its sensitivity to BSM physics. As so far, one has not identified BSM physics, its effects, if they exist, must be very small. Consequently, it is of crucial importance to understand all normal, Standard Model, backgrounds to ever higher precision. In this context, Multi-Parton Interactions and in particular Double Parton Interactions are a major concern. These are processes in which two hard QCD reactions take place in the same proton-proton collision, such that (yet unknown) wave function effects become relevant. These are parameterized by Double-Parton Distributions (DPDs) and what we do is to calculate so-called invariant functions which in turn parameterize these DPDs. Because each quark can have 3 flavors and 3 spin orientations and one has two such quarks taking part in a double hard interaction there are many different DPDs and invariant functions, each of them being a function of several variables. Thus, DPDs contain very much information on the quark-gluon structure of a nucleon, of which one cannot predict which will become important for BSM searches but which makes them very interesting in their own right. We have pioneered the theoretical formalism which allows to calculate properties of DPDs using lattice QCD, which is the standard numerical method to calculate properties of specific hadrons from first principle Quantum Chromo Dynamics (QCD). As very little is known so far about DPDs, we focus for the time being on their general properties.

Fig.1 shows typical results for spin-independent invariant functions for the flavors down-down (dd), up-up (uu) and up-down (ud) combinations of quark flavors, as well as one interference DPD (yellow). Fig.2 shows the same when one of the quarks is spin polarized transversely to the proton momentum. The message of both figures is that we can determine all of these leading invariant functions of DPDs rather precisely. A proton has two valence up quarks but only one valence down quark, which explains why the up-up DPD is largest. Note that the interference DPD is not smaller in size than the down-down DPD.  Fig.3 compares results for one specific invariant function for two lattices with different lattice constants. Its message is that the dependence on the lattice constant is very weak. Fig.4 compares the results for different quark masses.

The message here is that it is important to extrapolate the quark masses to their physical values what we will focus on next. In the long term the task would then shift to calculating the invariant functions of higher Mellin moments of DPDs, but this is not part of the present project.

Abstract

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

Principal Investigator: Prof. Dr. Francesco Knechtli, Bergische Universität Wuppertal

Target system: parallel computer Fritz

Project Report (04/2023)

The charmonium spectrum is of great importance to both particle physics and astrophysics, as it provides crucial information about the properties of the strong force and the nature of quarks. In particular, the hyperfine splitting in charmonium has long been a challenge for theoretical calculations, and accurate predictions are essential for interpreting experimental data and testing the standard model. By using cutting-edge techniques and state-of-the-art supercomputers, we aim to obtain results that are much more accurate than previous calculations and include effects that were so far neglected like mixing with light hadrons. Our results will provide new insights into the fundamental properties of the strong force and the nature of quarks, and will enable more accurate interpretation of experimental data from particle accelerators.

Abstract

Sudden Cardiac Death caused by, for example, malignant ventricular arrhythmias, results in an estimated 600.000 deaths per year in the European Community alone. However, the chaotic electrical excitation wave dynamics underlying such arrhythmias as ventricular fibrillation is not fully understood. It is known that fragments of (unstable) spiral or scroll waves drive the spatiotemporal dynamics during ventricular fibrillation and prevent coherent mechanical contraction of the heart.

Current clinical treatment consists of the application of a high-energy defibrillation shock delivered either from an external device, or from implantable cardioverter defibrillators (ICDs). In both cases, patients suffer from significant side effects due to this treatment, including additional tissue damage and post-traumatic stress.

In the last decades, several groups have been working on the development of alternative control strategies, with the aim to significantly reduce these side effects while reliably terminating the arrhythmia and restoring sinus rhythm. Many of these approaches use sequences of electrical stimulations of lower amplitude, in contrast to the conventional treatment consisting of a single high energy defibrillation shock. For example, pulse trains of electric low intensity stimuli with a specific frequency were used to terminate atrial fibrillation and ventricular fibrillation. Also several stages of differently timed pulse sequences were combined or feedback strategies were used.

Scientific field: 310 Statistical Physics, Soft Matter, Biological Physics, Nonlinear Dynamics

Principal Investigator: Prof. Dr. Thomas Lilienkamp, Technische Hochschule Nürnberg

Target system: GPGPU cluster Alex

Abstract

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

Principal Investigator: Prof. Dr. Ana-Suncana Smith, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Abstract

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 fields: 310 Statistical Physics, Soft Matter, Biological Physics, Nonlinear Dynamics; 403/01 Chemical and Thermal Process Engineering

Principal Investigator: Prof. Dr. Michael Engel, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz & GPGPU cluster Alex

Project Report (06/2024)

Abstract

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 fields: 310 Statistical Physics; 404-03 Fluid Mechanics

Principal Investigator: Prof. Dr. Michael Wilczek, Universität Bayreuth

Target system: parallel computer Fritz & GPGPU cluster Alex

Abstract

Our project aims to address the computational challenges in simulating the response of neutrino telescopes, such as IceCube and KM3NeT, by developing a neural-network-based surrogate model. Traditional Monte Carlo simulations, particularly the simulation of Cherenkov light from high-energy neutrino interactions, are computationally intensive, often requiring extensive GPU resources. Our surrogate model will learn to parametrize detector responses, enabling more efficient event simulations. Additionally, it will facilitate design studies for next-generation detectors like P-ONE and IceCube-Gen2. By leveraging statistical tools, our framework will optimize detector design without the need for limiting the design parameter space or developing event selection and reconstruction algorithms.

Scientific field: 311 Astrophysics and Astronomy

Principal Investigator: Prof. Dr. Claudio Kopper, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Abstract

Red supergiant stars mark the late phases of massive stars. They are also the direct progenitors of Type II supernovae and gravitational wave sources. However, their mass loss mechanism remains elusive, largely due to the poorly-understood variable nature of their envelopes where convection interacts with large- amplitude pulsation. Such convective motions, driven by internal heating and external cooling, operate at a length scale comparable to the stellar radius. Therefore, global 3D simulations involving accurate radiation hydrodynamics are needed to model the inner structure and outer atmosphere of red supergiants. However, a grid of global red supergiant simulations that covers realistic stellar parameter space has never been computed before. Using the moving-mesh magnetohydrodynamic code AREPO, recently extended with the state-of-the-art radiation transport module, we perform a suite of global 3D radiation hydrodynamic simulations of red supergiant envelopes with updated physics. This suite will serve as the most up-to-date models to be compared with observations of red supergiants. It will also enable potential follow-up simulations of wind launching, shock breakout, common envelope, and binary mass transfer, which pose major uncertainties in our predictions of some transient events and gravitational wave sources.

Scientific field: 311 Astrophysics and Astronomy

Principal Investigator: Prof. Dr. Selma E. de Mink, Ludwig-Maximilians-Universität München

Target system: parallel computer Fritz

This project is already listed under Natural Sciences: Particles, Nuclei and Fields.

Abstract

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

Principal Investigator: Dr. Noé Brucy, Heidelberg University

Target system: parallel computer Fritz

Project Report (04/2024)

The Milky is a complex system, with several components (gas, stars, dark matter) interacting together. In particular, the gaseous disk is undergoing gravitational forces for the stellar components. To better understand this interaction, we ran simulations of hydrodynamical and magneto-hydrodymical simulations of a Milky-way like galaxies, imposing two different external gravitational potentials to the gas. We found that the details of the potential made little difference in the global star formation rate of the galaxies, but are crucial to explain where the star formation take place. In the future, we aim to better understand how the potential influence the kinematic of the gas, as well as how the orbit of the gas and the stars are affected.

Publications

G. H. Hunter, M. C. Sormani, J. P. Beckmann, E. Vasiliev, S. C. O. Glover, R. S. Klessen, J. D. Soler, N. Brucy, P. Girichidis, J. Goller, L. Ohlin, R. Tress, S. Molinari, O. Gerhard, M. Benedettini, R. Smith, P. Hennebelle, and L. Test. Testing kinematic distances under a realistic Galactic potential. Investigating systematic errors in the kinematic distance method arising from a non-axisymmetric potential. Astronomy & Astrophysics. DOI: 10.1051/0004-6361/202450000.

Abstract

This compute time project delivers the necessary amount of core hours needed to provide realistic results of finite element simulations of a laser beam welding process.

In our project we consider a laser beam welding process. Short cycle times and small heat-affected zones have made this noncontact joining process a very viable option in the course of the increasing degree of automation in industrial production. However, due to the high cooling rate inherent in the process solidification cracks can occur. A quantitative understanding of the development mechanisms of these fractures and their correlation to process parameters is thus indispensable for the development and improvement of this process. The final goal of the project is to provide a complete simulation of this process that will gives us indicators under which conditions a crack can appear. A multiscale approach is needed to represent the properties and the behavior of the material and moreover efficient solver strategies are indispensable.

The development of efficient and robust numerical solver strategies, the parallel implementation and integration thereof into the software libraries FE2TI and PETSc is essential. In this computing time period, first large scale production simulations of laser beam welding processes using the FE2TI software are planned as well as optimizations and scalability tests of the integrated Schwarz domain decomposition preconditioners/solvers.

Scientific fields: 312 Mathematik; 402 Mechanics and Constructive Mechanical Engineering

Principal Investigator: Prof. Dr. Axel Klawonn, University of Cologne

Target system: parallel computer Fritz

Abstract

The goal of the project is to perform scale-resolving simulations of turbulent flow in wall-bounded geometries to gain new understanding of the flow behavior near boundaries and support future method development. The project PDExa-CFD uses high-order discontinuous Galerkin finite element discretizations in space and splitting implicit/explicit methods in time to discretize the incompressible Navier-Stokes equations. As test case, direct numerical simulation of the flow over a periodic hill will be computed at a Reynolds number Re=19,000 as well as limit studies towards even higher Reynolds numbers.

Scientific fields: 312 Mathematik; 404-3 Fluid Mechanics

Principal Investigator: Prof. Dr. Martin Kronbichler, Ruhr-Universität Bochum

Target system: parallel computer Fritz & GPGPU cluster Alex

Abstract

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 fields: 402 Mechanik; 312 Mathematik

Principal Investigator: Prof. Dr. Axel Klawonn, University of Cologne

Target system: parallel computer Fritz & GPGPU cluster Alex

Project Report (06/2024)

1) Phamaco-mechanical FSI
In “A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methods, 2023” we presented our framework for the coupling between an antihypertensive drug modeled by a diffusion process and the mechanical response of an arterial wall modeled by a sophisticated smooth muscle model. The project can be divided into two parts. Part 1 being the realistic modeling of the arterial wall and accurately representing the effects certain drugs can have on the wall’s properties. Here, antihypertensive drugs were considered, which have an effect on the contractile behavior of the arterial wall, which in turn can lower the blood pressure. Consequently, a full pharmaco-mechanical fluid structure interaction (FSCI) framework was developed. With the previously granted compute time, numerous studies with respect to the mechanical properties were performed to analyze the influence of antihypertensive drugs on arterial wall. Figure 1 displays the blood pressure measured in the artery in a comparison with and without antihypertensive drug in our full FSCI framework. Furthermore, deeper numerical analysis especially with respect to more complex and realistic problem setting was carried out.

As such computations for full FSCI contain tens of thousands of time steps for modeling different stress and heart beat levels, scalable and efficient algorithms and solver strategies are necessary. This leads to Part 2 which focuses on developing such strategies. The previously mentioned, parallel scalable two-level overlapping Schwarz preconditioners with GDSW-type coarse spaces are implemented in the Trilinos package FROSch. The fully coupled FSCI system is solved iteratively via GMRES. As we can apply a block factorization to the system, we can precondition each arising sub problem with a different strategy. Consequently, with the previously granted compute time each subproblem was investigated separately to obtain the optimal solver configuration.

The monolithic pharmaco-mechanical system (SCI) was solved and investigated with respect to weak and strong scaling. The above mentioned paper contains the detailed description of the modeling of the arterial wall, the numerical treatment and solver strategies. Based on the results and the granted compute time the weak scaling for a test problem could be achieved (see Figure 2).

With respect to the fluid sub problem, the scaling properties were also analyzed. Here, the incompressible (generalized) Newtonian Navier-Stokes equations were considered. The weak scaling properties for the stationary case were shown for the backward-facing step example and a deeper analysis of the monolithic preconditioner was performed (see Figure 3; article is in preparation).

For the full FSCI problem, aside from the already presented qualitative results, further scaling tests combined with the results for the SCI and fluid subproblem will be performed.

2) Multiscale Model for Blood Flow
The current state of the research was partly presented at ECCOMAS in June 2024 with the title “Efficient Parallel Non-Newtonian Blood Flow Simulations in Trilinos – Towards Incorporating Microscopic Information with a Data-Driven Approach.” Generally the research can be subdivided into two main parts:

Incorporating Mescoscopic Information into Macroscopic Simulations for Blood Flow Predictions

The first part involves simulating blood flow on a macroscopic scale to efficiently and accurately estimate field variables by incorporating mesoscopic information. Our initial approach involves a shear-rate dependent generalized-Newtonian fluid model for blood to simulate its shear-thinning behavior. The needed viscosity functions were obtained from our partner group at Forschungszentrum Jülich, who developed a virtual rheometer based on their mesoscopic solver that captures red blood cell dynamics. There are different viscosity functions for blood, as the viscosity depends not only on shear rate but also on factors such as hematocrit and the specific mechanics of healthy or diseased cells, all of which can be modeled by our partner group. But as a first step these were kept constant. Using the shear-rate dependent viscosity functions and our efficient macroscopic solver, we are able to run blood flow simulations capturing the shear thinning behavior.

As supported by literature, we conclude that for larger vessels, the shear-thinning effect is relatively small and the limiting viscosity has the most dominant effect on flow features. However, as the vessel size decreases or in pathological cases such as severe stenosis with recirculation zones, the non-Newtonian effects become increasingly significant. In the future, we aim to investigate smaller vessels in the regime of hundreds of micrometers. For these vessels, the non-homogeneous distribution of red blood cells and their effects become more important and should be incorporated into the model by simulating a non-constant hematocrit profile.

Numerical Aspects of Simulating Non-Newtonian fluid flow

The second part of our research focuses on efficient simulation using scalable solvers. For this, we employ monolithic overlapping Schwarz preconditioners from the FROSCH package to solve the saddle point problem resulting from the discretization of incompressible, stationary flow of generalized-Newtonian fluids. For the nonlinear problem, we use the (globalized) Newton’s method from the NOX package in Trilinos.

Building on the results of previous projects by Lea Saßmannshausen et al., we used different coarse basis combinations to solve a backward-facing step flow case. These combinations, implemented in FROSCH, were applied to generalized-Newtonian fluid flow using viscosity functions of varying strengths (see Figure 5). Compared to Newtonian flow, we observed that reusing the coarse basis becomes increasingly inefficient, making it beneficial to compute the coarse basis based on the updated linearization after each Newton iteration.

In Figure 6, only the best combination, GDSW* for velocity and RGDSW for pressure, is indicated but all possible combinations were computed. We conclude that a combination of a large coarse space for velocity and a smaller for pressure is most efficient, as stated in the results of Lea Saßmannshausen et al. In future work, we aim to investigate the convergence of the nonlinear solver, as we have observed that for the strongest nonlinearity in viscosity, Newton’s method does not converge. Additionally, we will examine how a restart affects the reuse of the coarse basis.

Abstract

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 fields: 404-03 Fluid Mechanics; 312 Mathematics

Principal Investigator: Prof. Dr. Stefan Turek, TU Dortmund University

Target system: parallel computer Fritz & GPGPU cluster Alex

Abstract

Scientific fields: 313 Atmospheric Science, Oceanography and Climate Research; 409 Computer Science

Principal Investigator: Prof. Dr. Andreas Hotho, Julius-Maximilians-Universität Würzburg

Target system: GPGPU cluster Alex

Abstract

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

Principal Investigator: Prof. Dr. Thomas Mölg, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: parallel computer Fritz

Project Report (03/2024)

In the first 1.5 years, several successful simulations were conducted in the project ATMOS. These were mostly based on the numerical atmospheric model WRF, a well-known community model. The main progress has comprised (i) the development of new modules for WRF to implement important (yet thus far, neglected) processes, especially with regard to cold climates; (ii) the creation of a new regional climate data set for New Zealand over the period 2005-2020 with a focus on the Southern Alps; and (iii) insights into cloud processes that are tied to radiation effects and the isotopic composition of water.

Publications

M. Andernach, J. Turton, and T. Mölg: Modeling cloud properties over the 79N Glacier (Nioghalvfjerdsfjorden, NE Greenland) for an intense summer melt period in 2019. Quarterly Journal of the Royal Meteorological Society 148, 2022. DOI: 10.1002/qj.4374.

E. Kropač, T. Mölg, and N. J. Cullen: A new, high-resolution atmospheric dataset for southern New Zealand, 2005–2020. Geoscience Data Journal 11(4), 2024. DOI: 10.1002/gdj3.263.

M. Saigger, T. Sauter, C. Schmid, E. Collier, B. Goger, G. Kaser, R. Prinz, A. Voordendag, and T. Mölg: A Drifting and Blowing Snow Scheme in the Weather Research and Forecasting model. Journal of Advances in Modeling Earth Systems 16(6), 2024. DOI: 10.1029/2023MS004007.

N. Landshuter, F. Aemisegger, T. Mölg: Stable Water Isotope Signals and Their Relation to Stratiform and Convective Precipitation in the Tropical Andes. Journal of Geophysical Research Atmospheres 129(14), 2024. DOI: 10.1029/2023JD040630.

Abstract

With the advent of the digital era, especially the widespread adoption of Web 2.0 platforms, social media has emerged as a new avenue for studying urban morphological transformations. Traditionally, urban morphology focuses on interpreting the physical structures of cities, such as buildings, streets, and public spaces, which are tangible evidences of cities as human habitats. However, modern cities are not only collections of physical spaces but also venues for cultural and emotional exchanges. This study utilizes social media data as a tool to investigate the relationship between urban form and residents’ opinions and emotions. By comparing social media discussions from different types of urban morphology, this study reveals the potential impacts of physical environments on residents’ opinions. This research not only enriches the scope of traditional urban morphology studies but also offers new perspectives and data support for urban planning and
environmental policies.

Scientific fields: 315-02 Geophysics and Geodesy; 410-02 Construction Engineering and Architecture

Principal Investigator: Dr. Richard Lemoine Rodriguez, Julius-Maximilians-Universität Würzburg

Target system: parallel computer Fritz & GPGPU cluster Alex

This project is already listed under Natural Sciences: Molecular Chemistry.

Abstract

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

Principal Investigator: Prof. Dr. Stephan Kümmel, Universität Bayreuth

Target system: parallel computer Fritz

Abstract

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

Principal Investigator: Prof. Dr. Bernd Engels, Julius-Maximilians-Universität Würzburg

Target system: GPGPU cluster Alex

Project Report not yet available

Abstract

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 fields: 303 Physical and Theoretical Chemistry; 307 Condensed Matter Physics

Principal Investigator: Prof. Dr. Matthias Scheffler, TU Berlin

Target system: parallel computer Fritz

Project Report not yet available

Abstract

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

Principal Investigator: Dr. Chris Byrohl, Heidelberg University

Target system: parallel computer Fritz

Project Report (09/2024)

cosmosTNG is a first-of-its-kind constrained cosmological galaxy formation simulation exploring the formation processes of our Universe in its first billions of years after the Big Bang. An observed patch of the sky is first used to reconstruct the matter distribution shortly after the Big Bang, which is then evolved with the state-of-the-art structure formation code AREPO using the TNG galaxy formation model. The simulation outcomes enable a direct comparison of simulations and observations within the COSMOS field on the night sky and provide a new tool to explore galaxy formation in realistic large-scale environments, as well as a probe of the physics and distribution of gas in and around galaxies two billion years after the Big Bang. First results indicate similar outcomes to “conventional” cosmological galaxy formation simulations, albeit substantial differences for the abundance and properties of massive galaxies occur, potentially pointing at the uniqueness of the overdense COSMOS field.

Publications

C. Byrohl, D. Nelson, B. Horowitz, K.-G. Lee, and A. Pillepich: Introducing cosmosTNG: simulating galaxy formation with constrained realizations of the COSMOS field. arXiv, 2024. DOI: 10.48550/arXiv.2409.19047.

Abstract

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

Principal Investigator: Prof. Dr. Volker Springel, Ludwig-Maximilians-Universität München

Target system: parallel computer Fritz

Project Report (07/2024)

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 encountering 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. We found that three-body encounters can produce varieties of outcomes, some of which are observable transients, such as stellar disruption, collisions between stars, and collisions between stars and black holes. See Figure 1 as an example.

The two decisive parameters for determining the outcomes are the binary phase angle, determining which two objects meet at the first closest approach, and the impact parameter, demarcating the boundary between violent and non-violent interactions. When the impact parameter is smaller than the binary semimajor axis, tidal disruptions and star-black hole collisions frequently occur, while the two stars mostly merge when the two stars meet first instead. In both cases, the black holes typically are surrounded by an optically and geometrically thick accretion disk and accrete at super-Eddington rates, possibly generating luminous flares. The stellar collision products either form a binary with the black hole or remain unbound to the black hole. Upon collision, the merged stars are hotter and larger than main sequence stars of the same mass at similar age. Even after recovering their thermal equilibrium state, stellar collision products, if isolated, would remain hotter and brighter than main sequence stars until becoming giants.

Project Accomplishments

The overarching goal of the proposed research program is to conduct detailed hydrodynamics calculations for multi-body encounters. Specifically, the three main objectives are 1) to identify types of outcomes, 2) estimate their observational signature, and 3) improve our current understanding based on the point-particle approximation.

The three most significant impacts of the projects are the identification of transients produced in various types of three-body encounters and their classifications, and the examination of observational features of potentially observable transient phenomena. More specifically, we were able to identify the ranges of key parameters where observable transients can be potentially created and the dependence of outcome properties on encounter parameters. In addition, the simulations of tidal disruption events in active galactic nuclei (Ryu, McKernan, Ford et al. MNRAS 527, 8103 (2024)) was the first global simulations of tidal disruption events in gaseous environments where the initial approach of the star was successfully modelled. In that paper, we found the characteristic density above which the evolution of debris becomes qualitatively different from those in gas-free tidal disruption events, which includes the mixing of debris into the disk as depicted in Figure 2. Our semi-analytic model for debris evolution and the characteristic density can be used to place constraints on the density of active galactic nuclei disks, which otherwise is hard to achieve.

We made two significant improvements from previous work. First, the hydrodynamics scheme adopted for our simulations overcomes numerical limits of widely used methods. The hydrodynamics code AREPO adopts the moving-mesh approach which inherits advantages of the Eulerian finite-volume method and the Lagrangian smoothed particle method. This allows us to account for shocks and mixing more accurately. Second, we adopt realistic initial conditions. The initial state of the stars has been often approximated as the solution of the gravitational Poisson’s equation for polytropic gas in hydrostatic equilibrium even though those models sometime show significant discrepancies from detailed stellar evolution models. In our projects, the initial state of the stars was taken from stellar models generated using the stellar evolution code MESA.

In this project, we have published five papers published in a peer-reviewed journal (see Publications below).

The computation resources were sufficiently large to complete the proposed projects. The code speed with AREPO was much faster than that on other clusters that PI have been running  simulations on, which facilitated the progress of the projects substantially.

Publications

T. Ryu, R. Perna, R. Pakmor, J.-Z. Ma, R. Farmer, and S. de Mink: Close encounters of tight binary stars with stellar-mass black holes. MNRAS 519(4), 2023. DOI: 10.1093/mnras/stad079

T. Ryu, R. Valli, R. Pakmor, R. Perna, S. de Mink, and V. Springel: Close encounters of black hole—star binaries with stellar-mass black holes. MNRAS 525(4), 2023. DOI: 10.1093/mnras/stad1943

T. Ryu, S. de Mink, R. Farmer, R. Pakmor, R. Perna, and V. Springel: Close encounters of star–black hole binaries with single stars. MNRAS 527(2) 2024. DOI: 10.1093/mnras/stad3082

T. Ryu, B. McKernan, S. Ford, M. Cantiello, M. Graham, D. Stern, and N. Leigh: In-plane tidal disruption of stars in discs of active galactic nuclei. MNRAS 527(3), 2024. DOI: 10.1093/mnras/stad3487

T. Ryu, P. Amaro Seoane, A. Taylor, and S. Ohlmann: Collisions of red giants in galactic nuclei. MNRAS 528(4), 2024. DOI: 10.1093/mnras/stae396

Abstract

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

Principal Investigator: Dr. Samuel Spencer, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Project Report (01/2024)

Imaging Air Cherenkov Telescopes (IACTs) are fundamental to ground-based observations of the very high-energy sky. To perform ground-based gamma-ray astronomy, an effective rejection of the hadronic background is pivotal. Within the current research project, we are investigating algorithms based on state-of-the-art machine-learning techniques to improve instrument resolution and sensitivity.

We proposed a new deep-learning-based algorithm for classifying images by interpreting the detected images as sets of triggered sensors, which are then represented as graphs and analyzed using graph convolutional networks. This innovative method, particularly effective for images cleaned of night sky light, circumvents challenges associated with sparse images encountered in traditional deep learning techniques like convolutional neural networks. Our exploration of various graph network architectures has yielded promising results, showcasing improvements over existing machine-learning and deep-learning-based methods. This memory-efficient approach has promising potential to be adapted to IACT arrays with a large number of telescopes.

For precise observations using IACTs the instrument response has to be derived, requiring large simulation libraries. These simulations, characterized by computationally and memory-intensive calculations, are repeatedly performed for different observation intervals to account for variations in the optical sensitivity of the telescopes. Leveraging generative models based on deep neural networks, specifically Wasserstein Generative Adversarial Networks, we propose an efficient method for storing extensive simulation libraries in a memory-efficient manner and rapidly generating simulations. In our study, we applied this approach to a state-of-the-art IACT camera featuring over 1,500 pixels, and demonstrated the successful generation of high-quality images with high fidelity with respect to important physical parameters. Notably, the substantial increase in simulation speed, on the order of 100,000, holds promising implications for efficient and fast simulations for IACTs.

The project is still ongoing under the name: GAMMAML.

Publications

J. Glombitza, V. Joshi, B. Bruno, and S. Funk:  Application of graph networks to background rejection in Imaging Air Cherenkov Telescopes. Journal of Cosmology and Astroparticle Physics 11, 2023. DOI: 10.1088/1475-7516/2023/11/008.

C. Elflein, S. Funk, and J. Glombitza: Ultra-fast generation of air shower images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks. Journal of Instrumentation 19, 2024. DOI: 10.1088/1748-0221/19/04/P04010.

Abstract

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

Principal Investigator: PD Dr. Thomas Eberl, Friedrich-Alexander-Universität Erlangen-Nürnberg

Target system: GPGPU cluster Alex

Project Report (11/2023)

Different optimization steps for training Graph Neural Networks (GNNs) on the task of tau neutrino event identification in KM3NeT/ORCA were performed. The last optimization step consisted of an automatic hyperparameter optimization (AHPO) that systematically searched through the hyperparameter space by stepwise parameter selection and subsequent automated training of the resulting GNNs. The chosen hyperparameters, various metrics that were monitored during training, and the final performance metric of the GNNs were logged. In total, about 2000 GNNs could be trained with the 46000 GPU-hours of computing time granted for this project. The data from this AHPO was used to identify the most significant hyperparameters, i.e., the hyperparameters whose values have the greatest influence on the resulting performance of the GNNs.

The most significant hyperparameter was identified to be the learning rate. The boxplot on the left shows the distribution of the value of the performance metrics over many trained GNNs for learning rates in different ranges. It indicates that learning rates between 10-3 and 10-2 result in the best-performing GNNs. Analogous investigations were done for many other hyperparameters in the GNN architecture, demonstrating which of these are essential and which are of less significance for the performance in the studied classification task. Importantly, this systematic analysis a-posteriori confirmed the general, best-guess hyperparameter choices that were made in previous KM3NeT studies, published in two dissertations and several conference proceedings, using very similar GNN architectures for classification and regression tasks. Due to a lack of corresponding resources within the international KM3NeT Collaboration, this important systematic study could not be done earlier. Another goal of the AHPO was to achieve a performance gain in classification compared to the first-step manual optimizations done earlier. However, the AHPO did not show a significant performance gain over the manual optimizations. Presumably, the reason is that the GNN architecture was already well-optimized due to its use in previous studies. Finally, using the insights, strategies, and methods developed during this project for future architectures and applications in the analysis of neutrino telescope data, more efficient, faster and more trustable optimizations will be possible.

The resources granted to this project have been sufficient to achieve its goals. The baseline target was to train between 1000-1500 different GNNs, but the resources turned out to be even enough to train about 2000 GNNs. This number of GNNs allowed us to compute a large enough sample to draw statistically relevant conclusions on the significance of most of the probed hyperparameters. The AHPO ran for most of the time in parallel on 64 GPUs, i.e., on eight computing nodes with eight GPUs per node.

Publications

The accomplishments of this project are documented in the master’s thesis of Lukas Hennig.

Abstract

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

Principal Investigator: PD Dr. Wolfgang Söldner, University of Regensburg

Target system: parallel computer Fritz

Project Report (01/2024)

This project is already listed under Natural Sciences: Chemistry.

Scientific field: 307-02 Theoretical Condensed Matter Physics

University: RWTH Aachen

Target system: parallel computer Fritz

Scientific field: 307-02 Theoretical Condensed Matter Physics

University: Julius-Maximilians-Universität Würzburg

Target system: parallel computer Fritz