Search

Machine-learning interatomic potentials (MLPs) are fast, data-driven surrogate models of atomistic systems' potential energy surfaces that can accelerate ab-initio molecular dynamics (MD) simulations by several orders of magnitude. The performance of MLPs is commonly measured as the prediction error in energies and forces on data not used in their training. While low prediction errors on a test set are necessary, they do not guarantee good performance in MD simulations. The latter requires physically motivated performance measures obtained from running accelerated simulations. However, the adoption of such measures has been limited by the effort and domain knowledge required to calculate and interpret them. To overcome this limitation, we present a benchmark that automatically quantifies the performance of MLPs in MD simulations of a liquid-liquid phase transition in hydrogen under pressure, a challenging benchmark system. The benchmark's h-llpt-24 dataset provides reference geometries, energies, forces, and stresses from density functional theory MD simulations at different temperatures and mass densities. The benchmark's Python code automatically runs MLP-accelerated MD simulations and calculates, quantitatively compares and visualizes pressures, stable molecular fractions, diffusion coefficients, and radial distribution functions. Employing this benchmark, we show that several state-of-the-art MLPs fail to reproduce the liquid-liquid phase transition.

Coherent Diffraction Imaging (CDI) is an experimental technique to get images of isolated structures by recording the light scattered off the sample. Thanks to the extremely bright and short coherent light pulses provided by X-ray Free Electron Lasers, CDI makes it possible to study nanostructures in the gas phase and get time-resolved snapshots of their ultrafast dynamics with unprecedented resolution. In principle, the sample density can be recovered from the scattered light field through a straightforward Fourier Transform operation. However, only the amplitude of the field is recorded, while the phase is lost during the measurement process and has to be retrieved by means of suitable, well-established, phase retrieval algorithms. We present the Memetic Phase Retrieval (MPR) method, an improved approach to the phase retrieval problem, which makes use of a combination of existing phase retrieval algorithms and evolutionary algorithms to mitigate the shortcomings of conventional approaches. We benchmark the method on experimental data acquired in two experimental campaigns at SwissFEL and European XFEL. Imaging results on isolated nanostructures reveal considerable stability of the algorithm's behavior on the input parameters, as well as the capability of identifying the solution in challenging conditions. A user-friendly implementation of the MPR method is released as open-source software, aiming at being a reference tool for the FEL imaging community., Comment: 37 pages, 13 figures. Software not yet released as open source. Software documentation under construction. Authors list up to date

Learning fine-scale details of a coastal ocean simulation from a coarse representation is a challenging task. For real-world applications, high-resolution simulations are necessary to advance understanding of many coastal processes, specifically, to predict flooding resulting from tsunamis and storm surges. We propose a Deep Network for Coastal Super-Resolution (DNCSR) for spatiotemporal enhancement to efficiently learn the high-resolution numerical solution. Given images of coastal simulations produced on low-resolution computational meshes using low polynomial order discontinuous Galerkin discretizations and a coarse temporal resolution, the proposed DNCSR learns to produce high-resolution free surface elevation and velocity visualizations in both time and space. To efficiently model the dynamic changes over time and space, we propose grid-aware spatiotemporal attention to project the temporal features to the spatial domain for non-local feature matching. The coordinate information is also utilized via positional encoding. For the final reconstruction, we use the spatiotemporal bilinear operation to interpolate the missing frames and then expand the feature maps to the frequency domain for residual mapping. Besides data-driven losses, the proposed physics-informed loss guarantees gradient consistency and momentum changes. Their combination contributes to the overall 24% improvements in RMSE. To train the proposed model, we propose a large-scale coastal simulation dataset and use it for model optimization and evaluation. Our method shows superior super-resolution quality and fast computation compared to the state-of-the-art methods., Comment: 13 pages, 12 figures

The energetically most efficient way how a deformed red blood cell regains equilibrium is mathematically described by the gradient flow of the Canham-Helfrich functional, including a spontaneous curvature and the conservation of surface area and enclosed volume. Using a recently discovered multiplicity inequality, we prove global existence and convergence of smooth solutions for spheres and axisymmetric tori, provided the initial energy lies below explicit thresholds., Comment: 33 pages. Comments welcome!

In this study, we elaborate on the concept of scalable anomalous reflector (AR) to analyze the angular response, frequency response, and spatial scalability of a designed AR across a broad range of angles and frequencies. We utilize theoretical models and ray tracing simulations to investigate the communication performance of two different-sized scalable finite ARs, one smaller configuration with 48 x 48 array of unit cells and the other constructed by combining four smaller ARs to form a larger array with 96 x 96 unit cells. To validate the developed theoretical approach, we conducted measurements in an auditorium to evaluate the received power through an AR link at different angles and frequencies. In addition, models of scalable deflectors are implemented in the MATLAB ray tracer to simulate the measurement scenario. The results from theoretical calculations and ray tracing simulations achieve good agreement with measurement results., Comment: 9 pages, 12 figures

Despite tremendous progress, machine learning and deep learning still suffer from incomprehensible predictions. Incomprehensibility, however, is not an option for the use of (deep) reinforcement learning in the real world, as unpredictable actions can seriously harm the involved individuals. In this work, we propose a genetic programming framework to generate explanations for the decision-making process of already trained agents by imitating them with programs. Programs are interpretable and can be executed to generate explanations of why the agent chooses a particular action. Furthermore, we conduct an ablation study that investigates how extending the domain-specific language by using library learning alters the performance of the method. We compare our results with the previous state of the art for this problem and show that we are comparable in performance but require much less hardware resources and computation time., Comment: Accepted at: The Fifteenth International Conference on Swarm Intelligence (ICSI'2024)

This paper considers the numerical solution of Timoshenko beam network models, comprised of Timoshenko beam equations on each edge of the network, which are coupled at the nodes of the network using rigid joint conditions. Through hybridization, we can equivalently reformulate the problem as a symmetric positive definite system of linear equations posed on the network nodes. This is possible since the nodes, where the beam equations are coupled, are zero-dimensional objects. To discretize the beam network model, we propose a hybridizable discontinuous Galerkin method that can achieve arbitrary orders of convergence under mesh refinement without increasing the size of the global system matrix. As a preconditioner for the typically very poorly conditioned global system matrix, we employ a two-level overlapping additive Schwarz method. We prove uniform convergence of the corresponding preconditioned conjugate gradient method under appropriate connectivity assumptions on the network. Numerical experiments support the theoretical findings of this work., Comment: 26 pages, 4 figures

Achieving optimal balance in games is essential to their success, yet reliant on extensive manual work and playtesting. To facilitate this process, the Procedural Content Generation via Reinforcement Learning (PCGRL) framework has recently been effectively used to improve the balance of existing game levels. This approach, however, only assesses balance heuristically, neglecting actual human perception. For this reason, this work presents a survey to empirically evaluate the created content paired with human playtesting. Participants in four different scenarios are asked about their perception of changes made to the level both before and after balancing, and vice versa. Based on descriptive and statistical analysis, our findings indicate that the PCGRL-based balancing positively influences players' perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios., Comment: 4 pages, 2 figures, 1 table. Accepted at the IEEE Conference on Games (IEEE CoG) 2024

Graph data structures offer a versatile and powerful means to model relationships and interconnections in various domains, promising substantial advantages in data representation, analysis, and visualization. In games, graph-based data structures are omnipresent and represent, for example, game economies, skill trees or complex, branching quest lines. With this paper, we propose G-PCGRL, a novel and controllable method for the procedural generation of graph data using reinforcement learning. Therefore, we frame this problem as manipulating a graph's adjacency matrix to fulfill a given set of constraints. Our method adapts and extends the Procedural Content Generation via Reinforcement Learning (PCGRL) framework and introduces new representations to frame the problem of graph data generation as a Markov decision process. We compare the performance of our method with the original PCGRL, the run time with a random search and evolutionary algorithm, and evaluate G-PCGRL on two graph data domains in games: game economies and skill trees. The results show that our method is capable of generating graph-based content quickly and reliably to support and inspire designers in the game creation process. In addition, trained models are controllable in terms of the type and number of nodes to be generated., Comment: 8 pages, 7 figures, 2 tables. Accepted at the IEEE Conference on Games (IEEE CoG) 2024

We propose a way to maintain strong consistency and facilitate error analysis in the context of dissipation-based WENO stabilization for continuous and discontinuous Galerkin discretizations of conservation laws. Following Kuzmin and Vedral (J. Comput. Phys. 487:112153, 2023) and Vedral (arXiv preprint arXiv:2309.12019), we use WENO shock detectors to determine appropriate amounts of low-order artificial viscosity. In contrast to existing WENO methods, our approach blends candidate polynomials using residual-based nonlinear weights. The shock-capturing terms of our stabilized Galerkin methods vanish if residuals do. This enables us to achieve improved accuracy compared to weakly consistent alternatives. As we show in the context of steady convection-diffusion-reaction (CDR) equations, nonlinear local projection stabilization terms can be included in a way that preserves the coercivity of local bilinear forms. For the corresponding Galerkin-WENO discretization of a CDR problem, we rigorously derive a priori error estimates. Additionally, we demonstrate the stability and accuracy of the proposed method through one- and two-dimensional numerical experiments for hyperbolic conservation laws and systems thereof. The numerical results for representative test problems are superior to those obtained with traditional WENO schemes, particularly in scenarios involving shocks and steep gradients.

A survey of the contributions to the Journal of Chemical Physics' Special Topic on Software for Atomistic Machine Learning.

We propose numerical algorithms for recovering parameters in eigenvalue problems for linear elasticity of transversely isotropic materials. Specifically, the algorithms are used to recover the elastic constants of a rotor core. Numerical tests show that in the noiseless setup, two pairs of bending modes are sufficient for recovering one to four parameters accurately. To recover all five parameters that govern the elastic properties of electric engines accurately, we require three pairs of bending modes and one torsional mode. Moreover, we study the stability of the inversion method against multiplicative noise; for tests in which the data contained multiplicative noise of at most $1\%$, we find that all parameters can be recovered with an error less than $10\%$.

Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even minor numerical adjustments, which may have an unexpected impact on the overall gaming experience. Consequently, thorough manual testing and fine-tuning during development are essential. Unlike existing works that address algorithmic balancing for specific games or genres, this work adopts a more abstract approach, focusing on game balancing through its economy, detached from a specific game. We propose GEEvo (Game Economy Evolution), a framework to generate graph-based game economies and balancing both, newly generated or existing economies. GEEvo uses a two-step approach where evolutionary algorithms are used to first generate an economy and then balance it based on specified objectives, such as generated resources or damage dealt over time. We define different objectives by differently parameterizing the fitness function using data from multiple simulation runs of the economy. To support this, we define a lightweight and flexible game economy simulation framework. Our method is tested and benchmarked with various balancing objectives on a generated dataset, and we conduct a case study evaluating damage balancing for two fictional economies of two popular game character classes., Comment: 8 pages, 4 figures, 3 tables. Accepted at IEEE Congress on Evolutionary Computation (IEEE CEC) 2024

We provide sharp sufficient criteria for an integral $2$-varifold to be induced by a $W^{2,2}$-conformal immersion of a smooth surface. Our approach is based on a fine analysis of the Hausdorff density for $2$-varifolds with critical integrability of the mean curvature and a recent local regularity result by Bi-Zhou. In codimension one, there are only three possible density values below $2$, each of which can be attained with equality in the Li--Yau inequality for the Willmore functional by the unit sphere, the double bubble, and the triple bubble. We show that below an optimal threshold for the Willmore energy, a varifold induced by a current without boundary is in fact a curvature varifold with a uniform bound on its second fundamental form. Consequently, the minimization of the Willmore functional in the class of curvature varifolds with prescribed even Euler characteristic provides smooth solutions for the Willmore problem. In particular, the "ambient" varifold approach and the "parametric" approach are equivalent for minimizing the Willmore energy., Comment: 28 pages, 3 figures. Comments welcome!

Spatially selective melting of metal materials by laser irradiation allows for the precise welding as well as the 3D printing of complex metal parts. However, the simple scanning of a conventional Gaussian beam typically results in a melt track with randomly distributed surface features due to the complex and dynamic behavior of the melt pool. In this study, the implications of utilizing a dynamically oscillating energy input on driving melt track fluctuations is investigated. Specifically, the laser intensity and/or intensity distribution is sinusoidally modulated at different scan speeds, and the effect of modulation frequency on the resulting surface features of the melt track is examined. The formation of periodically oriented surface features indicates an evident frequency coupling between the melt pool and the modulation frequency. Moreover, such a frequency coupling becomes most prominent under a specific modulation frequency, suggesting resonant behavior. The insights provided in this study will enable the development of novel methods, allowing for the control and/or mitigation of inherent fluctuations in the melt pool through laser-driven resonances.

As an alternative to the metastability of the electroweak vacuum, resulting from perturbative calculations, one can consider a non-perturbative effective potential which, as at the beginning of the Standard Model, is restricted to the pure $\Phi^4$ sector yet consistent with the known analytical and numerical studies. In this approach, where the electroweak vacuum is now the lowest-energy state, besides the resonance of mass $m_h=$ 125 GeV defined by the quadratic shape of the potential at its minimum, the Higgs field should exhibit a second resonance with mass $(M_H)^{\rm Theor}=690\,(30)$ GeV associated with the zero-point energy determining the potential depth. In spite of its large mass, this resonance would couple to longitudinal $W$s with the same typical strength as the low-mass state at 125 GeV and represent a relatively narrow resonance, mainly produced at LHC by gluon-gluon fusion. In this Letter, we review LHC data suggesting a new resonance of mass $(M_H)^{\rm EXP} \sim 682\,(10)$ GeV, with a statistical significance that is far from negligible., Comment: 5 pages, 5 tables, 7 figures, accepted for publication in LHEP. arXiv admin note: substantial text overlap with arXiv:2308.01429

I comment on a paper by Christoph Hanhart and Alexey Nefediev, published in Phys. Rev. D 106, 114003 (2022). The authors discuss the interpretation of mesons close to their lowest decay threshold and present a mechanism for the formation of molecular states. The proposed formalism is then applied to the axial-vector mesons $D_{s1}(2536)$ and $D_{s1}(2460)$, presenting two scenarios for the lighter meson, namely a $D^\star K$ molecule or a compact $c\bar{s}$ state. The authors argue that the latter hypothesis requires a fine-tuning of the mixing angle between the $J^{PC}=1^{++}$ and $J^{PC}=1^{+-}$ $C$-parity eigenstates. In this Comment I show that no such fine-tuning is needed, as demonstrated in an article published in Phys. Rev. D 84, 094020 (2011), where a unitarized quark model was applied to the two $C$-parity eigenstates, coupled to several two-meson channels including $D^\star K$. The coupled-channel dynamics naturally leads to a mixing angle very close to the required one. Moreover, I argue that the $D_1(2420)$ and $D_1(2430)$ axial-vectors, not considered by the authors, as well as a lattice simulation in Phys. Rev. D 90, 034510 (2014), also not mentioned by the authors, do not lend support to a molecular interpretation of the $D_{s1}(2460)$. I conclude with some more general remarks about mesons coupling to $S$-wave thresholds., Comment: 3 pages, 1 figure; comment on arXiv:2209.10165

We prove the uniform convergence of the geometric multigrid V-cycle for hybrid high-order (HHO) and other discontinuous skeletal methods. Our results generalize previously established results for HDG methods, and our multigrid method uses standard smoothers and local solvers that are bounded, convergent, and consistent. We use a weak version of elliptic regularity in our proofs. Numerical experiments confirm our theoretical results.

In this paper, we systematically study the electromagnetic (EM) and communication aspects of an RIS through EM simulations, system-level and ray-tracing simulations, and finally measurements. We simulate a nearly perfect, lossless RIS, and a realistic lossy anomalous reflector (AR) in different ray tracers and analyze the large-scale fading of simple RIS-assisted links. We also compare the results with continuous and quantized unit cell reflection phases with one to four-bit resolutions. Finally, we perform over-the-air communication link measurements in an indoor setting with a manufactured sample of a wide-angle AR. The EM, system-level, and ray-tracing simulation results show good agreement with the measurement results. It is proved that the introduced macroscopic model of RIS from the EM aspects is consistent with our proposed communication models, both for an ideal RIS and a realistic AR., Comment: 10 pages, 14 figures

This work aims to build connections between the electromagnetic and communication aspects of Reconfigurable Intelligent Surfaces (RIS) by proposing a methodology to combine outputs from electromagnetic RIS design into an RIS-tailored system-level simulator and a ray tracer. In this first part of the contribution, a periodic anomalous reflector is designed using an algebraic array antenna scattering synthesis technique that enables electromagnetically accurate modeling of scattering surfaces with both static and reconfigurable scattering characteristics. The multi-mode periodic structure, capable of scattering into several anomalous angles through manipulation of reactive loads, is then cropped into finite-sized arrays, and the quantization effects of the load reactances on the array scattering are analyzed. An experimental anomalous reflector is demonstrated with a comparison between simulated and measured scattering performance. In the second part, the simulated receiving and transmitting scattering patterns of the anomalous reflector are utilized to build an electromagnetically consistent path loss model of an RIS into a system-level simulator. Large-scale fading is analyzed in simple scenarios of RIS-assisted wireless networks to verify the communication model, and an indoor scenario measurement using the manufactured anomalous reflector sample to support the simulation analysis. After verifying the connections between electromagnetic and communication aspects through simulations and measurements, the proposed communication model can be used for a broad range of RIS designs to perform large-scale system-level and ray-tracing simulations in realistic scenarios., Comment: 10 pages, 7 figures

The photoconversion of CO$_2$ to hydrocarbons is a sustainable route to its transformation into value-added compounds and, thereby, crucial to mitigating the energy and climate crises. CuPt nanoparticles on TiO$_2$ surfaces have been reported to show promising photoconversion efficiency. For further progress, a mechanistic understanding of the catalytic properties of these CuPt/TiO$_2$ systems is vital. Here, we employ $\textit{ab-initio}$ calculations, machine learning, and photocatalysis experiments to explore their configurational space and examine their reactivity and find that the interface plays a key role in stabilizing *CO$_2$, *CO, and other CH-containing intermediates, facilitating higher activity and selectivity for methane. A bias-corrected machine-learning interatomic potential trained on density functional theory data enables efficient exploration of the potential energy surfaces of numerous CO$_2$@CuPt/TiO$_2$ configurations via basin-hopping Monte Carlo simulations, greatly accelerating the study of these photocatalyst systems. Our simulations show that CO$_2$ preferentially adsorbs at the interface, with C atom bonded to a Pt site and one O atom occupying an O-vacancy site. The interface also promotes the formation of *CH and *CH$_2$ intermediates. For confirmation, we synthesize CuPt/TiO$_2$ samples with a variety of compositions and analyze their morphologies and compositions using scanning electron microscopy and energy-dispersive X-ray spectroscopy, and measure their photocatalytic activity. Our computational and experimental findings qualitatively agree and highlight the importance of interface design for selective conversion of CO$_2$ to hydrocarbons., Comment: Main text: 16 pages and 7 figures; Supporting information: 10 pages and 9 figures; Page 1, affiliation re-ordering; Page 4, typos corrected and abbreviation defined; Page 5, Table 1 caption revised and typos corrected; Page 16 typos corrected

Resonance pole positions of the $f_0(500)$ alias $\sigma(500)$ meson are computed and plotted as a continuous function of pion mass in the framework of a unitary and analytic coupled-channel model for scalar mesons as dynamical $q\bar{q}$ states. The $\sigma$ is described with a light and a strange $q\bar{q}$ seed, mixing with each other mainly through the common $\pi\pi$, $K\bar{K}$, and $\eta\eta$ meson-meson channels. The few model parameters are fitted to experimental $S$-wave $\pi\pi$ phase shifts up to 1 GeV, yielding, in the case of the physical pion mass, resonance poles at $(460-i222)$ MeV for the $\sigma(500)$ and $(978-i37)$ MeV for the $f_0(980)$. Resonance, bound-state, and virtual-state pole trajectories are shown as a function of $m_\pi$ running from 139.57 MeV to 1 GeV. These are compared to recent lattice QCD computations that use interpolating fields corresponding to the model's channels, i.e., for a few discrete $m_\pi $values., Comment: 6 pages, 4 figures, LaTeX style of Acta Physica Polonica B, writeup of talk by George Rupp at the Excited QCD 2024 Workshop, 14-20 January 2024 in Benasque, Spain

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter summarizing contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, this highlights the impact of material property variation on predicted artery stresses and presents a pipeline to explore and characterize uncertainty in computational biomechanics., Comment: To appear: Biomechanics and Modeling in Mechanobiology

Resonance, bound-state, and virtual-state pole positions of the $f_0(500)$ scalar meson are computed as a continuous function of pion mass in the framework of a unitarized and analytic coupled-channel model for scalar mesons, described as dynamical quark-antiquark states. The $f_0(500)$ is modeled with both light and strange $q\bar{q}$ seeds, mixing with each other through the common $S$-wave $\pi\pi$, $K\bar{K}$, and $\eta\eta$ meson-meson decay channels. The few model parameters are fitted to experimental $S$-wave $\pi\pi$ phase shifts up to 1 GeV. In the case of the physical $\pi^\pm$ mass of 139.57 MeV, resonance poles at $(460-i222)$ MeV and $(978-i37.2)$ MeV are found for the $f_0(500)$ and $f_0(980)$, respectively. Resonance, bound-state, and virtual-state pole trajectories are computed and plotted as a function of pion masses up to 500 MeV, both in the complex-energy and complex-momentum planes. The results are discussed and compared to the most advanced lattice QCD computations employing interpolators that correspond to the $q\bar{q}$ and meson-meson channels in the present model, that is, for a few discrete values of the unphysical pion mass in those lattice calculations., Comment: revtex4-2, 7 pages, 7 figures. V2: 2 very recent lattice references and corresponding discussion added, 1 figure removed; version accepted for publication in Phys. Rev. D as a regular article

Because of their high photon flux, X-ray free-electron lasers (FEL) allow to resolve the structure of individual nanoparticles via coherent diffractive imaging (CDI) within a single X-ray pulse. Since the inevitable rapid destruction of the sample limits the achievable resolution, a thorough understanding of the spatiotemporal evolution of matter on the nanoscale following the irradiation is crucial. We present a technique to track X-ray induced structural changes in time and space by recording two consecutive diffraction patterns of the same single, free-flying nanoparticle, acquired separately on two large-area detectors opposite to each other, thus examining both the initial and evolved particle structure. We demonstrate the method at the extreme ultraviolet (XUV) and soft X-ray Free-electron LASer in Hamburg (FLASH), investigating xenon clusters as model systems. By splitting a single XUV pulse, two diffraction patterns from the same particle can be obtained. For focus intensities of about $2\cdot10^{12}\,\text{W/cm}^2$ we observe still largely intact clusters even at the longest delays of up to 650 picoseconds of the second pulse, indicating that in the highly absorbing systems the damage remains confined to one side of the cluster. Instead, in case of five times higher flux, the diffraction patterns show clear signatures of disintegration, namely increased diameters and density fluctuations in the fragmenting clusters. Future improvements to the accessible range of dynamics and time resolution of the approach are discussed.

Magnetism in two-dimensional materials reveals phenomena distinct from bulk magnetic crystals, with sensitivity to charge doping and electric fields in monolayer and bilayer van der Waals magnet CrI3. Within the class of layered magnets, semiconducting CrSBr stands out by featuring stability under ambient conditions, correlating excitons with magnetic order and thus providing strong magnon-exciton coupling, and exhibiting peculiar magneto-optics of exciton-polaritons. Here, we demonstrate that both exciton and magnetic transitions in bilayer and trilayer CrSBr are sensitive to voltage-controlled field-effect charging, exhibiting bound exciton-charge complexes and doping-induced metamagnetic transitions. Moreover, we demonstrate how these unique properties enable optical probes of local magnetic order, visualizing magnetic domains of competing phases across metamagnetic transitions induced by magnetic field or electrostatic doping. Our work identifies few-layer CrSBr as a rich platform for exploring collaborative effects of charge, optical excitations, and magnetism., Comment: 18 pages, 11 figures

Long-range, terrestrial quantum networks will require high brightness single-photon sources emitting in the telecom C-band for maximum transmission rate. Many applications additionally demand triggered operation with high indistinguishability and narrow spectral linewidth. This would enable the efficient implementation of photonic gate operations and photon storage in quantum memories, as for instance required for a quantum repeater. Especially, semiconductor quantum dots (QDs) have shown these properties in the near-infrared regime. However, the simultaneous demonstration of all these properties in the telecom C-band has been elusive. Here, we present a coherently (incoherently) optically-pumped narrow-band (0.8 GHz) triggered single-photon source in the telecom C-band. The source shows simultaneously high single-photon purity with $g^{(2)}(0) = 0.026$ ($g^{(2)}(0) = 0.014$), high two-photon interference visibility of 0.508 (0.664) and high application-ready rates of 0.75 MHz (1.45 MHz) of polarized photons. The source is based on a QD coupled to a circular Bragg grating cavity combined with spectral filtering. Coherent (incoherent) operation is performed via the novel SUPER scheme (phonon-assisted excitation).

We present low-temperature magnetoconductance measurements on Bi$_{1.5}$Sb$_{0.5}$Te$_{1.8}$Se$_{1.2}$ kinks with ribbon-shaped legs. The conductance displays a clear dependence on the in-plane magnetic field orientation. The conductance modulation is consistent with orbital effect-driven trapping of the topological surface states on different side facets of the legs of the kink, which affects their transmission across the kink. This magnetic field-driven trapping and conductance pattern can be explained with a semiclassical picture and is supported by quantum transport simulations. The interpretation is corroborated by varying the angle of the kink and analyzing the temperature dependence of the observed magnetoconductance pattern, indicating the importance of phase coherence along the cross section perimeter of the kink legs., Comment: Main Text (7 pages, 4 figures) and Supplemental Material (3 pages, 3 figures)

Valuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-devices like smartphones. To facilitate such an analysis, we present a toolchain called PrivAgE for a distributed, privacy-preserving aggregation of local data by taking the limited resources of edge-devices into account. The distributed aggregation is based on secure summation and simultaneously satisfies the notion of differential privacy. In this way, other parties can neither learn the sensitive data of single clients nor a single client's influence on the final result. We perform an evaluation of the power consumption, the running time and the bandwidth overhead on real as well as simulated devices and demonstrate the flexibility of our toolchain by presenting an extension of the summation of histograms to distributed clustering.

We report on time-resolved coherent diffraction imaging of gas-phase silver nanoparticles, strongly heated via their plasmon resonance. The x-ray diffraction images reveal a broad range of phenomena for different excitation strengths, from simple melting over strong cavitation to explosive disintegration. Molecular dynamics simulations fully reproduce this behavior and show that the heating induces rather similar trajectories through the phase diagram in all cases, with the very different outcomes being due only to whether and where the stability limit of the metastable superheated liquid is crossed., Comment: 17 pages, 8 figures (including supplemental material)

We present a computational design method that optimizes the reinforcement of dental prostheses and increases the durability and fracture resistance of dentures. Our approach optimally places reinforcement, which could be implemented by modern multi-material, three-dimensional printers. The study focuses on reducing deformation by identifying regions within the structure that require reinforcement (E-glass material). Our method is applied to a three-dimensional removable lower jaw dental prosthesis and aims to improve the living quality of denture patients and pretend fracture of dental reinforcement in clinical studies. To do this, we compare the deformation results of a non-reinforced denture and a reinforced denture that has two materials. The results indicate the maximum deformation is lower and node-based displacement distribution demonstrates that the average displacement distribution is much better in the reinforced denture.

The elastic energy of a bending-resistant interface depends both on its geometry and its material composition. We consider such a heterogeneous interface in the plane, modeled by a curve equipped with an additional density function. The resulting energy captures the complex interplay between curvature and density effects, resembling the Canham-Helfrich functional. We describe the curve by its inclination angle, so that the equilibrium equations reduce to an elliptic system of second order. After a brief variational discussion, we investigate the associated nonlocal $L^2$-gradient flow evolution, a coupled quasilinear parabolic problem. We analyze the (non)preservation of quantities such as convexity, positivity, and symmetry, as well as the asymptotic behavior of the system. The results are illustrated by numerical experiments., Comment: 34 pages, 13 figures. Final version. To appear in SIAM J. Math. Anal

Perturbative calculations predict that the Standard Model (SM) effective potential should have a new minimum, well beyond the Planck scale, much deeper than the electroweak vacuum. As it is not obvious that gravitational effects can get so strong to stabilize the potential, most authors have accepted the metastability scenario in a cosmological perspective. This perspective is needed to explain why the theory remains trapped into our electroweak vacuum, but requires to control the properties of matter in the extreme conditions of the early universe. Alternatively, one can consider the completely different idea of a non-perturbative effective potential which, as at the beginning of the SM, is restricted to the pure $\Phi^4$ sector yet consistent with the now existing analytical and numerical studies. In this approach, where the electroweak vacuum is the lowest-energy state, besides the resonance of mass $m_h=125$ GeV defined by the quadratic shape of the potential at its minimum, the Higgs field should exhibit a second resonance with mass $690\pm10({\rm stat})\pm20({\rm sys})$ GeV associated with the zero-point energy determining the potential depth. Despite its large mass, this would couple to longitudinal $W$s with the same typical strength as the low-mass state at 125 GeV and represent a relatively narrow resonance of width $\Gamma_H=30\div 38$ GeV, mainly produced at LHC by gluon-gluon fusion. So it is interesting that, in the LHC data, one can find various indications for a new resonance in the expected mass range with a non-negligible statistical significance. As this could become an important new discovery by just adding two missing samples of RUN2 data, we outline further refinements of the theoretical predictions that could be obtained by implementing unitarity constraints, in the presence of fermion and gauge fields, with coupled-channel calculations used for meson spectroscopy., Comment: Plain LaTeX, 36 pages, 12 figures, 4 tables; v2: plain two-column LaTeX, 13 figures, 7 tables, more discussion of theory and of experimental data; v3: final version accepted for publication in European Physical Journal C, with additional analysis and discussion

We present a symmetrization routine that optimizes and eases the analysis of data featuring the anomalous Hall effect. This technique can be transferred to any hysteresis with (point-)symmetric behaviour. The implementation of the method is demonstrated exemplarily using intermixed longitudinal and transversal data obtained from a chromium-doped ternary topological insulator revealing a clear hysteresis. Furthermore, by introducing a mathematical description of the anomalous Hall hysteresis based on the error function precise values of the height and coercive field are determined., Comment: 6 pages, 6 figures, supplementary information include

We formulate and analyze a multiscale method for an elliptic problem with an oscillatory coefficient based on a skeletal (hybrid) formulation. More precisely, we employ hybrid discontinuous Galerkin approaches and combine them with the localized orthogonal decomposition methodology to obtain a coarse-scale skeletal method that effectively includes fine-scale information. This work is the first step in reliably merging hybrid skeletal formulations and localized orthogonal decomposition to unite the advantages of both strategies. Numerical experiments are presented to illustrate the theoretical findings.

Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engineering relies on fundamental data science and software engineering skills. At the same time, model-based engineering is gaining relevance for the engineering of complex systems. In previous work, a model-based engineering approach integrating the formalization of machine learning tasks using the general-purpose modeling language SysML is presented. However, formalized machine learning tasks still require the implementation in a specialized programming languages like Python. Therefore, this work aims to facilitate the implementation of data-driven engineering in practice by extending the previous work of formalizing machine learning tasks by integrating model transformation to generate executable code. The method focuses on the modifiability and maintainability of the model transformation so that extensions and changes to the code generation can be integrated without requiring modifications to the code generator. The presented method is evaluated for feasibility in a case study to predict weather forecasts. Based thereon, quality attributes of model transformations are assessed and discussed. Results demonstrate the flexibility and the simplicity of the method reducing efforts for implementation. Further, the work builds a theoretical basis for standardizing data-driven engineering implementation in practice.