arXiv Physics @arxiv_physics@qoto.org

We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further enables transmission of the virus. We establish a sufficient condition for exponentially fast eradication of a virus for: 1) time-invariant graphs, 2) time-varying graphs with symmetric interactions between individuals and homogeneous virus spread across the network (same healing and infection rate for all individuals), and 3) directed and slowly varying graphs with heterogeneous virus spread (not necessarily same healing and infection rates for all individuals) across the network. Numerical examples illustrate our theoretical results and indicate that, for the time-varying case, violation of the aforementioned sufficient conditions could lead to the persistence of a virus.

Atoms confined in optical tweezer arrays constitute a platform for the implementation of quantum computers and simulators. State-dependent operations are realized by exploiting electrostatic dipolar interactions that emerge, when two atoms are simultaneously excited to high-lying electronic states, so-called Rydberg states. These interactions also lead to state-dependent mechanical forces, which couple the electronic dynamics of the atoms to their vibrational motion. We explore these vibronic couplings within an artificial molecular system -- a Rydberg tweezer molecule -- in which Rydberg states are excited under so-called facilitation conditions. This system undergoes a structural transition between an equilateral triangle and an equal-weighted superposition of distorted triangular states (Jahn-Teller regime) exhibiting spin-phonon entanglement on a micrometer distance. This highlights the potential of Rydberg tweezer arrays for the study of molecular phenomena at exaggerated length scales.

Nuclear energy has been gaining momentum recently as one of the solutions to tackle climate change. However, significant environmental and health-risk concerns remain associated with potential accidents. Despite significant preventive efforts, we must acknowledge that accidents may happen and, therefore, develop strategies and technologies for mitigating their consequences. In this paper, we review the Fukushima Dai-ichi Nuclear Power Plant accident, synthesize the time series and accident progressions across relevant disciplines, including in-plant physics and engineering systems, operators' actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. In light of the latest observations and simulation studies, we identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We propose new research and development directions to improve the resilience of nuclear power plants, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.

In this paper, a non-intrusive reduced-order model (ROM) for parametric reactor kinetics simulations is presented. Time-dependent ROMs are notoriously data intensive and difficult to implement when nonlinear multiphysics phenomena are considered. These challenges are exacerbated when parametric dependencies are included. The proper orthogonal decomposition mode coefficient interpolation (POD-MCI) ROM presented in this work can be constructed directly from lower-dimensional quantities of interest (QoIs) and is independent of the underlying model. This greatly alleviates the data requirement of many existing ROMs and can be used without modification on arbitrarily complex models or experimental data. The POD-MCI ROM is demonstrated on a number of examples and yields accurate characterizations of the QoIs within the selected parameter spaces at extremely attractive computational speed-up factors relative to the full-order models (FOMs).

Understanding multibody interactions between colloidal particles out of equilibrium has a profound impact on dynamical processes such as colloidal self assembly. However, traditional colloidal interactions are effectively quasi-static on colloidal timescales and cannot be modulated out of equilibrium. A mechanism to dynamically tune the interactions during colloidal contacts can provide new avenues for self assembly and material design. In this work, we develop a framework based on polymer-coated colloids and demonstrate that in-plane surface mobility and mechanical relaxation of polymers at colloidal contact interfaces enable an effective, dynamic interaction. Combining analytical theory, simulations, and optical tweezer experiments, we demonstrate precise control of dynamic pair interactions over a range of pico-Newton forces and seconds timescales. Our model may be used to engineer colloids with exquisite control over the kinetics and thermodynamics of colloidal self-assembly dynamics via interface modulation and nonequilibrium processing.

Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables. Despite their success, PROMs can be susceptible to inaccuracies, even instabilities, due to the improper accounting of the interaction between the resolved and unresolved scales of the multiscale system (known as the closure problem). In the current work, we interpret closure as a multifidelity problem and use a multifidelity deep operator network (DeepONet) framework to address it. In addition, to enhance the stability and/or accuracy of the multifidelity-based closure, we employ the recently developed "in-the-loop" training approach from the literature on coupling physics and machine learning models. The resulting approach is tested on shock advection for the one-dimensional viscous Burgers equation and vortex merging for the two-dimensional Navier-Stokes equations. The numerical experiments show significant improvement of the predictive ability of the closure-corrected PROM over the un-corrected one both in the interpolative and the extrapolative regimes.

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel methods. Our scheme is an approximate realization of the power method, where supervised learning is used to learn the next step of the power iteration. We show that the ground state properties of arbitrary gapped quantum hamiltonians can be reached with polynomial resources under the assumption that the supervised learning is efficient. Using kernel ridge regression, we provide numerical evidence that the learning assumption is verified by applying our scheme to find the ground states of several prototypical interacting many-body quantum systems, both in one and two dimensions, showing the flexibility of our approach.

The worldline formalism has previously been used for deriving compact master formulas for the QED $N$ - photon amplitudes in vacuum, in a constant field and in a plane-wave field. Here we carry this program one step further by deriving master formulas for the scalar and spinor QED $N$-photon amplitudes in the background of the "parallel" special case of a combined constant and plane-wave field.

The generation of plasma from hypervelocity impacts is an active research topic due to its important science and engineering ramifications in various applications. Previous studies have mainly focused on the ionization of the solid materials that constitute the projectile and the target. In this letter, we consider impact events that occur in a fluid (e.g.,~gas) medium, and present a multiphysics computational modeling approach and associated analysis to predict the behavior of the dynamic fluid-solid interaction that causes the surrounding fluid to ionize. The proposed computational framework is applied to a specific case involving a system of three interacting domains: a copper rod projectile impacting onto a soda lime glass target in a neon gas environment. The impact velocity is varied between 3 km/s and 6 km/s in different simulations. The computational model couples the compressible inviscid Navier-Stokes equations with the Saha ionization equations. The three material interfaces formed among the projectile, the target, and the ambient gas are tracked implicitly by solving two level set equations that share the same velocity field. The mass, momentum, and energy fluxes across the interfaces are computed using the FInite Volume method with Exact two-material Riemann problems (FIVER). The simulation result reveals a region of neon gas with high velocity, temperature, pressure, and mass density, formed in the early stage of the impact mainly due to the hypersonic compression of the fluid between the projectile and the target. For impact velocities higher than 4 km/s, ionization is predicted in this region.

Single layers of transition metal dichalcogenides, such as WSe$_2$ have gathered increasing attention due to their intense electron-hole interactions, being considered promising candidates for developing novel optical applications. Within the few-layer regime, these systems become highly sensitive to the surrounding environment, enabling the possibility of using a proper substrate to tune desired aspects of these atomically-thin semiconductors. In this scenario, the dielectric environment provided by the substrates exerts significant influence on electronic and optical properties of these layered materials, affecting the electronic band-gap and the exciton binding energy. However, the corresponding effect on the luminescence of transition metal dichalcogenides is still under discussion. To elucidate these impacts, we used a broad set of materials as substrates for single-layers of WSe$_2$, enabling the observation of these effects over a wide range of electrical permittivities. Our results demonstrate that an increasing permittivity induces a systematic red-shift of the optical band-gap of WSe$_2$, intrinsically related to a considerable reduction of the luminescence intensity. Moreover, we annealed the samples to ensure a tight coupling between WSe$_2$and its substrates, reducing the effect of undesired adsorbates trapped in the interface. Ultimately, our findings reveal how critical the annealing temperature can be, indicating that above a certain threshold, the heating treatment can induce adverse impacts on the luminescence. Furthermore, our conclusions highlight the influence the dielectric properties of the substrate have on the luminescence of WSe$_2$, showing that a low electrical permittivity favours preserving the native properties of the adjacent monolayer

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents for a given sample, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.

Moiré engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting electrons confined to a moiré superlattice potential minimum (the moiré atom) using a 2D fermionic neural network. We show that strong Coulomb interactions in combination with the anisotropic moiré potential lead to striking ``Wigner molecule" charge density distributions observable with scanning tunneling microscopy.

The entropic lattice Boltzmann framework proposed the construction of the discrete equilibrium by taking into consideration minimization of a discrete entropy functional. The effect of this form of the discrete equilibrium on properties of the resulting solver has been the topic of discussions in the literature. Here we present a rigorous analysis of the hydrodynamics and numerics of the entropic. In doing so we demonstrate that the entropic equilibrium features unconditional linear stability, in contrast to the conventional polynomial equilibrium. We reveal the mechanisms through which unconditional linear stability is guaranteed, most notable of which the adaptive normal modes propagation velocity and the positive-definite nature of the dissipation rates of all eigen-modes. We further present a simple local correction to considerably reduce the deviations in the effective bulk viscosity.

Neural-network quantum molecular dynamics (NNQMD) simulations based on machine learning are revolutionizing atomistic simulations of materials by providing quantum-mechanical accuracy but orders-of-magnitude faster, illustrated by ACM Gordon Bell prize (2020) and finalist (2021). State-of-the-art (SOTA) NNQMD model founded on group theory featuring rotational equivariance and local descriptors has provided much higher accuracy and speed than those models, thus named Allegro (meaning fast). On massively parallel supercomputers, however, it suffers a fidelity-scaling problem, where growing number of unphysical predictions of interatomic forces prohibits simulations involving larger numbers of atoms for longer times. Here, we solve this problem by combining the Allegro model with sharpness aware minimization (SAM) for enhancing the robustness of model through improved smoothness of the loss landscape. The resulting Allegro-Legato (meaning fast and "smooth") model was shown to elongate the time-to-failure $t_\textrm{failure}$, without sacrificing computational speed or accuracy. Specifically, Allegro-Legato exhibits much weaker dependence of timei-to-failure on the problem size, $t_{\textrm{failure}} \propto N^{-0.14}$ ($N$ is the number of atoms) compared to the SOTA Allegro model $\left(t_{\textrm{failure}} \propto N^{-0.29}\right)$, i.e., systematically delayed time-to-failure, thus allowing much larger and longer NNQMD simulations without failure. The model also exhibits excellent computational scalability and GPU acceleration on the Polaris supercomputer at Argonne Leadership Computing Facility. Such scalable, accurate, fast and robust NNQMD models will likely find broad applications in NNQMD simulations on emerging exaflop/s computers, with a specific example of accounting for nuclear quantum effects in the dynamics of ammonia.

Spectroscopy and imaging in the mid-infrared (2.5 $μ$m $\sim$ $λ$ $\sim$ 25 $μ$m) is bedevilled by the presence of a strong 300 K thermal background at room temperature that makes IR detectors decades noisier than can be readily achieved in the visible. The technique of "imaging with undetected photons" (IUP) exploits the quantum correlations between entangled photon pairs to transfer image information from one spectral region to another, and here we show that it does so in a way that is immune to the thermal background. This means that IUP can be used to perform high speed photon counting measurements across the mid-IR, using uncooled visible detectors that are many times cheaper, faster, and more sensitive than their IR counterparts.

The recent application of concepts from condensed-matter physics to photoelectron spectroscopy (PES) of volatile, liquid-phase systems has enabled the measurement of electronic energetics of liquids on an absolute scale. Particularly, vertical ionization energies, VIEs, of liquid water and aqueous solutions, both in the bulk and at associated interfaces, can now be routinely determined. These IEs are referenced to the local vacuum level, which is the appropriate quantity for condensed matter with associated surfaces, including liquids. Here, we connect this newly accessible energy level to another important surface property, namely, the solution work function, e$Φ_{liq}$. We lay out the prerequisites for and unique challenges of determining e$Φ$ of aqueous solutions and liquids in general. We demonstrate - for a model aqueous solution with a tetra-n-butylammonium iodide (TBAI) surfactant solute - that concentration-dependent work functions, associated with the surface dipoles generated by the segregated interfacial layer of TBA$^+$ and I$^-$ions, can be accurately measured under controlled conditions. We detail the nature of surface potentials, uniquely tied to the nature of the flowing-liquid sample, which must be eliminated or quantified to enable such measurements. This allows us to refer measured spectra of aqueous solutions to the Fermi level and quantitatively assign surfactant concentration-dependent spectral shifts to competing work function and electronic-structure effects, the latter determining, e.g., (electro)chemical reactivity. We describe the extension of liquid-jet PES to quantitatively access concentration-dependent surface descriptors that have so far been restricted to solid-phase measurements. These studies thus mark the beginning of a new era in the characterization of the interfacial electronic structure of aqueous solutions and liquids more generally.

In this article, tunable surface plasmon polaritons (SPPs) in graphene-based elliptical waveguides containing gyro-electric layers are investigated. The general structure has an elliptical cross-section, where each gyro-electric layer is surrounded by two graphene layers. The DC magnetic bias is applied on the z-axis. As a special case, a new plasma-based elliptical structure with double-layer graphene is studied in this paper. The figure of merit (FOM) for this waveguide can be varied by changing the magnetostatic bias and the chemical doping. At the frequency of 40 THz, the FOM of 139 for this waveguide is reported for the B= 1 T and μc=0.9 eV. Ability to adjust and tune the propagating properties of SPPs in hybrid graphene-plasma elliptical structures can be exploited for the design of new plasmonic components in the THz spectral region.

Nobel laureate Wolfgang Paul showed, back in the 1950s, that charged particles can be trapped using alternating electric fields. This technique is commonly referred to as Paul traps or radiofrequency traps (RF-traps) and is used in various areas of modern physics. This paper presents a 3D-printed mechanical Paul trap, a naïve simulation of the system in Python, and student investigations. The files for the 3D-printed trap are available for download and print, and the code for the simulation is available to run and tinker with.

The IBEX-Lo instrument on the Interstellar Boundary Explorer (IBEX) mission measures interstellar neutral (ISN) helium atoms. The detection of helium atoms is made through negative hydrogen (H$^-$) ions sputtered by the helium atoms from the IBEX-Lo conversion surface. The energy spectrum of ions sputtered by ISN helium atoms is broad and overlaps the four lowest IBEX-Lo electrostatic analyzer (ESA) steps. Consequently, the energy response function for helium atoms does not correspond to the nominal energy step transmission. Moreover, laboratory calibration is incomplete because it is difficult to produce narrow-energy neutral atom beams that are expected for ISN helium atoms. Here, we analyze the ISN helium observations in ESA steps 1-4 to derive the relative in-flight response of IBEX-Lo to helium atoms. We compare the ratios of the observed count rates as a function of the mean ISN helium atom energy estimated using the Warsaw Test Particle Model (WTPM). The WTPM uses a global heliosphere model to calculate charge exchange gains and losses to estimate the secondary ISN helium population. We find that the modeled mean energies of ISN helium atoms, unlike their modeled fluxes, are not very sensitive to the very local interstellar medium parameters. The obtained relative responses supplement the laboratory calibration and enable more detailed quantitative studies of the ISN helium signal. A similar procedure that we applied to the IBEX-Lo observations may be used to complement laboratory calibration of the next-generation IMAP-Lo instrument on the Interstellar Mapping and Acceleration Probe (IMAP) mission.

A new method for directly sampling the neutron resonance upscattering effect is presented. Alternatives have relied on inefficient rejection sampling techniques or large tabular storage of relative velocities. None of these approaches, which require pointwise energy data, are particularly well suited to the windowed multipole cross section representation. The new method called multipole analytic resonance scattering (MARS) overcomes these limitations by inverse transform sampling from the target relative velocity distribution where the cross section is expressed in the multipole formalism. The closed form relative speed distribution contains a novel special function we deem the incomplete Faddeeva function: $$ w(z, x) = \frac{i}π \int_{-\infty}^x \frac{e^{-t^2} dt}{z-t}. $$ We present the first results on its efficient numerical evaluation.