Computing and Compressing Electron Repulsion Integrals on FPGAs - Wu, Xin; Kenter, Tobias; Schade, Robert; Kühne, Thomas; Plessl, Christian (2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM))
The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic simulations. In practical simulations, several trillions of ERIs may have to be computed for every time step. In this work, we investigate FPGAs as accelerators for the ERI computation. We use template parameters, here within the Intel oneAPI tool flow, to create customized designs for 256 different ERI quartet classes, based on their orbitals. To maximize data reuse, all intermediates are buffered in FPGA on-chip memory with customized layout. The pre-calculation of intermediates also helps to overcome data dependencies caused by multi-dimensional recurrence relations. The involved loop structures are partially or even fully unrolled for high throughput of FPGA kernels. Furthermore, a lossy compression algorithm utilizing arbitrary bitwidth integers is integrated in the FPGA kernels. To our best knowledge, this is the first work on ERI computation on FPGAs that supports more than just the single most basic quartet class. Also, the integration of ERI computation and compression it a novelty that is not even covered by CPU or GPU libraries so far. Our evaluation shows that using 16-bit integer for the ERI compression, the fastest FPGA kernels exceed the performance of 10 GERIS ($10 × 10^9$ ERIs per second) on one Intel Stratix 10 GX 2800 FPGA, with maximum absolute errors around $10^{-7}$ - $10^{-5}$ Hartree. The measured throughput can be accurately explained by a performance model. The FPGA kernels deployed on 2 FPGAs outperform similar computations using the widely used libint reference on a two-socket server with 40 Xeon Gold 6148 CPU cores of the same process technology by factors up to 6.0x and on a new two-socket server with 128 EPYC 7713 CPU cores by up to 1.9x.

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics - Schade, Robert; Kenter, Tobias; Elgabarty, Hossam; Lass, Michael; Kühne, Thomas; Plessl, Christian (The International Journal of High Performance Computing Applications)
The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms.
Keywords: Hardware and Architecture; Theoretical Computer Science; Software

Modeling of Masked Droplet Deposition for Site-Controlled Ga Droplets - Feddersen, Stefan; Zolatanosha, Viktoryia; Alshaikh, Ahmed; Reuter, Dirk; Heyn, Christian (Nanomaterials)
Site-controlled Ga droplets on AlGaAs substrates are fabricated using area-selective deposition of Ga through apertures in a mask during molecular beam epitaxy (MBE). The Ga droplets can be crystallized into GaAs quantum dots using a crystallization step under As flux. In order to model the complex process, including the masked deposition of the droplets and a reduction of their number during a thermal annealing step, a multiscale kinetic Monte Carlo (mkMC) simulation of self-assembled Ga droplet formation on AlGaAs is expanded for area-selective deposition. The simulation has only two free model parameters: the activation energy for surface diffusion and the activation energy for thermal escape of adatoms from a droplet. Simulated droplet numbers within the opening of the aperture agree quantitatively with the experimental results down to the perfect site-control, with one droplet per aperture. However, the model parameters are different compared to those of the self-assembled droplet growth. We attribute this to the presence of the mask in close proximity to the surface, which modifies the local process temperature and the As background. This approach also explains the dependence of the model parameters on the size of the aperture.
Keywords: General Materials Science; General Chemical Engineering

Dispersion Energy‐Stabilized Boron and Phosphorus Lewis Pairs - Sieland, Benedikt; Stahn, Marcel; Schoch, Roland; Daniliuc, Constantin; Spicher, Sebastian; Grimme, Stefan; Hansen, Andreas; Paradies, Jan (Angewandte Chemie International Edition)
Keywords: General Chemistry; Catalysis

"Tankrabatt" und "Freiheitsenergie". Wie der Blog "neusprech.org" politische Euphemismen entlarvt - Tophinke, Doris (Zwischen Fake und Fakt)

Annotation uncertainty in the context of grammatical change - Merten, Marie-Luis; Wever, Marcel; Tophinke, Doris; Geierhos, Michaela; Hüllermeier, Eyke (International Journal of Corpus Linguistics)

Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions - Tornede, Alexander

Information Asymmetry and Gendered Behavior in Organizations and Digital Markets - Spiess-Bru, Clarissa

Automatisierte Abhängigkeitsanalyse von Anforderungen zur Wirkkettenmodellierung - Gräßler, Iris; Preuß, Daniel (Stuttgarter Symposium für Produktentwicklung SSP 2023)
Effect chain modelling is a method for creating information models for impact analyses of changes in system elements. For the estimation of change propagation, dependencies between requirements must be detected. The high number of require- ment dependencies in the engineering of complex technical systems results in the need for automation. In a study, it was shown that transformer models (BERT) are suitable for the automated dependency analysis of requirements. However, there are currently deficits in the applicability of the models for different projects without an extensive and heterogeneous training database. This paper investigates how active learning can be used to train BERT models (active-BERT) in order to increase the performance of the models for classifying requi- rement dependencies of projects with heterogeneous require- ments. The results show that the performance of the models increases significantly through active learning. Through active- BERT, engineers are enabled to model effect chains efficiently and to handle requirement changes effectively.

Ultrafast two-colour X-ray emission spectroscopy reveals excited state landscape in a base metal dyad - Nowakowski, Michał; Huber-Gedert, Marina; Elgabarty, Hossam; Kubicki, Jacek; Kertem, Ahmet; Lindner, Natalia; Khakhulin, Dimitry; Lima, Frederico Alves; Choi, Tae-Kyu; Biednov, Mykola; Piergies, Natalia; Zalden, Peter; et al. (arxiv)
Effective photoinduced charge transfer makes molecular bimetallic assemblies attractive for applications as active light induced proton reduction systems. For a more sustainable future, development of competitive base metal dyads is mandatory. However, the electron transfer mechanisms from the photosensitizer to the proton reduction catalyst in base metal dyads remain so far unexplored. We study a Fe-Co dyad that exhibits photocatalytic H2 production activity using femtosecond X-ray emission spectroscopy, complemented by ultrafast optical spectroscopy and theoretical time-dependent DFT calculations, to understand the electronic and structural dynamics after photoexcitation and during the subsequent charge transfer process from the FeII photosensitizer to the cobaloxime catalyst. Using this novel approach, the simultaneous measurement of the transient Kalpha X-ray emission at the iron and cobalt K-edges in a two-colour experiment is enabled making it possible to correlate the excited state dynamics to the electron transfer processes. The methodology, therefore, provides a clear and direct spectroscopic evidence of the Fe->Co electron transfer responsible for the proton reduction activity.

Variational Learning of Euler–Lagrange Dynamics from Data - Ober-Blöbaum, Sina; Offen, Christian (Journal of Computational and Applied Mathematics)
The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional. Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equations, are related to the existence of an action functional. Incorporating variational structure into learning algorithms for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predictions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this, we introduce and rigorously analyse the concept of inverse modified Lagrangians by developing an inverse version of variational backward error analysis. (3) Finally, we introduce a method to perform system identification from position observations only, based on variational backward error analysis.
Keywords: Lagrangian learning; variational backward error analysis; modified Lagrangian; variational integrators; physics informed learning

Backward error analysis for conjugate symplectic methods - McLachlan, Robert; Offen, Christian (Journal of Geometric Mechanics)
The numerical solution of an ordinary differential equation can be interpreted as the exact solution of a nearby modified equation. Investigating the behaviour of numerical solutions by analysing the modified equation is known as backward error analysis. If the original and modified equation share structural properties, then the exact and approximate solution share geometric features such as the existence of conserved quantities. Conjugate symplectic methods preserve a modified symplectic form and a modified Hamiltonian when applied to a Hamiltonian system. We show how a blended version of variational and symplectic techniques can be used to compute modified symplectic and Hamiltonian structures. In contrast to other approaches, our backward error analysis method does not rely on an ansatz but computes the structures systematically, provided that a variational formulation of the method is known. The technique is illustrated on the example of symmetric linear multistep methods with matrix coefficients.
Keywords: variational integrators; backward error analysis; Euler--Lagrange equations; multistep methods; conjugate symplectic methods

Hamiltonian Neural Networks with Automatic Symmetry Detection - Dierkes, Eva; Offen, Christian; Ober-Blöbaum, Sina; Flaßkamp, Kathrin (Chaos)
Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we enhance the HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples, a pendulum on a cart and a two-body problem from astrodynamics are considered.

6.3.2 Medienpädagogische Professionalisierung in der beruflichen Weiterbildung. Eine Studie aus Perspektive der biografischen Medienforschung. Steckbrief zur Dissertation. - Dehmel, Lukas (Grundlagen der Erwachsenen- und Weiterbildungsforschung)

„Allowing the Economy to Breathe Even During the Crisis“ – Interview with Tax Expert Caren Sureth-Sloane - Unterstell, Rembert (german research – Magazine of the Deutsche Forschungsgemeinschaft)

ENT | GRENZ | UNGEN in der Medienpädagogik. Perspektiven im Anschluss an den 28. Kongress der DGfE

Die Entgrenzung von Kommunikationskulturen in Lehrpersonenkollegien. Reflexion einer unbeabsichtigten Begleiterscheinung der Arbeit mit Tablets. - Dehmel, Lukas; Meister, Dorothee; Gerhardts, Lara (Zeitschrift MedienPädagogik)

Pop(musik)journalismen zwischen Printmagazin-Krise und postdigitaler Realität – Forschungsüberblick und multiperspektivische Thesen - Godlewsky, Tanja; Jacke, Christoph; Roenneke, Stefanie; Venker, Thomas (Musik und Internet. Aktuelle Phänomene populärer Kulturen)
Dieser Beitrag bietet verschiedene professionelle Perspektiven auf den popmusikalischen Kommunikationsprozesses innerhalb postdigitaler Popmusikjournalismuskulturen. Neben einer wissenschaftlichen Rahmung, Übersicht und Strukturierung des Untersuchungsfelds und des Forschungsstands versammelt der Beitrag multiperspektivische Thesen, die journalistische Praktiken und ihre Reflexionen von Produktion über Distribution und Rezeption/Nutzung bis zur Weiterverarbeitung inklusive Archivierung/Institutionalisierung/Musealisierung aufgreifen und herausstellen. Die aufgestellten Thesen gelten als Aufschlag für die zukünftige Analyse des so fundamentalen und doch auch immer wieder übersehenen Berufsfeldes und seiner Rezeption zwischen Faszination und Exploitation.

Versatile self-piercing riveting with a tumbling superimposed punch - Wituschek, S.; Elbel, L.; Lechner, M. (Materials Research Proceedings)
Abstract. Increasing resource efficiency is a major challenge and affects almost every aspect of social and economic life. The mobility sector in particular is responsible for a large share of primary energy consumption and is increasingly in the focus of public interest. One possibility to adress these challenges is to reduce the vehicle weight by means of lightweight construction technologies such as multi-material systems. These assemblies consist of workpieces with different mechanical and geometrical properties, which poses a major challenge for joining technology. Mechanical joining processes such as semi-tubular self-piercing riveting are often used in the production of these assemblies, but due to their process characteristics, they are rigid and can only react to changing process variables to a limited extent. One way to increase the versatility of self-piercing riveting is to superimpose a tumbling kinematics on the punch. During tumbling, an angular offset of the punch axis to the tool axis is set and the contact area between punch and workpiece is reduced. In this work, investigations were carried out to determine how the tumbling strategy, consisting of the parameters tumbling angle, tumbling onset and tumbling kinematics, affects the material flow of the rivet element. For this purpose, experimental tests are conducted with the typical materials of conventional multi-material systems and the geometric joint formations are determined by means of macrographs.

Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen - Sprenger, Alexander
Ever increasing demands on the performance of microchips are leading to ever more complex semiconductor technologies with ever shrinking feature sizes. Complex applications with high demands on safety and reliability, such as autonomous driving, are simultaneously driving the requirements for test and diagnosis of VLSI circuits. Throughout the life cycle of a microchip, uncertainties occur that affect its timing behavior. For example, weak circuit structures, aging effects, or process variations can lead to a change in the timing behavior of the circuit. While these uncertainties do not necessarily lead to a change of the functional behavior, they can lead to a reliability problem. With modular and hybrid compaction two test instruments are presented in this work that can be used for X-tolerant test response compaction in the built-in Faster-than-At-Speed Test (FAST) which is used to detect uncertainties in VLSI circuits. One challenge for test response compaction during FAST is the high and varying X-rate at the outputs of the circuit under test. By dividing the circuit outputs into test groups and separately compacting these test groups using stochastic compactors, the modular compaction is able to handle these high and varying X-rates. To deal with uncertainties on logic interconnects, a method for distinguishing crosstalk and process variation is presented. In current semiconductor technologies, the number of parasitic coupling capacitances between logic interconnects is growing. These coupling capacitances can lead to crosstalk, which causes increased current flow in the logic interconnects, which in turn can lead to increased electromigration. In the presented method, delay maps describing the timing behavior of the circuit outputs at different operating points are used to train artificial neural networks which classify the tested circuits into fault-free and faulty.
Keywords: Testantwortkompaktierung; Prozessvariation; Silicon Lifecycle Management