We simulate a hard-sphere liquid in confined geometry where the separation of the two parallel, hard walls is smaller than two particle diameters. By systematically reducing the wall separation we analyze the behavior of structural and thermodynamic properties, such as inhomogeneous density profiles, structure factors, and compressibilities when approaching the two-dimensional limit. In agreement with asymptotic predictions, we find for quasi-two-dimensional fluids that the density profile becomes parabolic and the structure factor converges toward its two-dimensional counterpart. To extract the compressibility in polydisperse samples a perturbative expression is used which qualitatively influences the observed nonmonotonic dependence of the compressibility with wall separation. We also present theoretical calculations based on fundamental-measure theory and integral-equation theory, which are in very good agreement with the simulation results.

We experimentally study the crack formation and decompaction-wave propagating in a vibrated powder bed consisting of glass beads of 5 $\mu \mathrm{m}$ in diameter. The vibrated powder bed exhibits three distinct phases depending on the vibration conditions: consolidation (CS), static fracture (SF), and dynamic fracture (DF). Particularly, we found an upward wave propagation in the DF regime when the powder bed is strongly vibrated. As a remarkable feature, we found that in fine cohesive powders, the decompaction-wave propagation speed normalized to gravitational speed is independent of the shaking strength. This result implies that the wave propagation speed is governed by the balance between gravity and cohesion effect rather than vibration strength. We also explore the universality of wave propagation phenomenon in coarser and low-density granular powders.

A paradigm shift in the physics of laser-plasma interactions is approaching with the commissioning of multipetawatt laser facilities worldwide. Radiation reaction processes will result in the onset of electron-positron pair cascades and, with that, the absorption and partitioning of the incident laser energy, as well as the energy transport throughout the irradiated targets. To accurately quantify these effects, one must know the focused intensity on target in situ. In this work, a way of measuring the focused intensity on target is proposed based upon the ionization of xenon gas at low ambient pressure. The field ionization rates from two works [Phys. Rev. A 59, 569 (1999) and Phys. Rev. A 98, 043407 (2018)], where the latter rate has been derived using quantum mechanics, have been implemented in the particle-in-cell code SMILEI [Comput. Phys. Commun. 222, 351 (2018)]. A series of one- and two-dimensional simulations are compared and shown to reproduce the charge states without presenting visible differences when increasing the simulation dimensionality. They provide a way to accurately verify the intensity on target using in situ measurements.

The same system can exhibit a completely different dynamical behavior when it evolves in equilibrium conditions or when it is driven out-of-equilibrium by, e.g., connecting some of its components to heat baths kept at different temperatures. Here we concentrate on an analytically solvable and experimentally relevant model of such a system—the so-called Brownian gyrator—a two-dimensional nanomachine that performs a systematic, on average, rotation around the origin under nonequilibrium conditions, while no net rotation takes place under equilibrium ones. On this example, we discuss a question whether it is possible to distinguish between two types of a behavior judging not upon the statistical properties of the trajectories of components but rather upon their respective spectral densities. The latter are widely used to characterize diverse dynamical systems and are routinely calculated from the data using standard built-in packages. From such a perspective, we inquire whether the power spectral densities possess some “fingerprint” properties specific to the behavior in nonequilibrium. We show that indeed one can conclusively distinguish between equilibrium and nonequilibrium dynamics by analyzing the cross-correlations between the spectral densities of both components in the short frequency limit, or from the spectral densities of both components evaluated at zero frequency. Our analytical predictions, corroborated by experimental and numerical results, open a new direction for the analysis of a nonequilibrium dynamics.

During descent, a single-winged maple seed (samara) can naturally reach a delicate equilibrium state, stable autorotation, before landing. This article reveals the intrinsic equilibrium of a particular type of samaras in terms of measurable aerodynamic and geometric parameters. To this end, we conducted a series of in situ measurements for the rate of vertical descent (exclusive of crosswind) of an autorotating samara in a natural range of samara sizes and masses. We then extended the range of size and mass by introducing artificial samaras, with discrete mass elements purposely designed to approximate the asymmetrical and nonuniform distribution of mass found with natural samaras. Based on the widened range, a fundamental nondimensional correlation of dynamic pressure and disc loading was generalized, where all stable autorotation descent profiles collapse to a single descent characteristic curve, irrespective of the size and mass of the natural and artificial samara's specimens. Results reveal that for stably autorotating (both natural and artificial) samaras, their terminal descent velocity (expressed as dynamic pressure) and disc loading attained equilibrium at a value that is inversely proportional to the coefficient of lift.

Colloidal particles with anisotropic interaction, such as Janus particles, are important model systems for anisotropic atoms and molecules. Janus particles in a single crystal can rotate collectively and form polycrystalline orientational domains as the temperature increases, while the lattice structure in the translational degree of freedom is preserved. Such an unusual solid-solid transition preserves the long-range translational order but loses the orientational order, and its mechanism is unclear. We find that the transition is induced by internal strains and the orientation-position coupling plays an essential role in the transition. We explain the mechanism using the anisotropic elasticity theory and derive the transition condition and the directions of the domain boundaries by analyzing the strain energy and the stress. The results of the molecular dynamics simulation are consistent with the theoretical analysis. Such a transition mechanism can exist in other anisotropic particle systems.

We introduce a model of active responsive colloids (ARCs) in which an internal degree of freedom (DoF) of a single colloidal particle is “activated” by coupling it to a different thermostat than for the translational DoFs. As for the responsive internal DoF, we consider specifically the size (diameter) of the spherical particles, which is confined by a harmonic parent potential being either entropic or energetic in nature. The ARCs interact via a repulsive Hertzian pair potential, appropriate to model hydrogels or elastic colloids, and are studied for various densities using Brownian dynamics simulations. We tune the internal activity in the nonequilibrium steady state by scanning through a wide range of internal temperatures, both smaller (“colder”) and larger (“hotter”) than the translational temperature. The results show a rich and intriguing behavior for the emergent property distributions, colloidal pair structure, and the diffusive translational dynamics controlled by the internal activity, substantially depending on whether the internal DoF moves in an entropic or energetic potential. We discuss theoretical thermal limits and phenomenological models which can explain some of the nonequilibrium trends qualitatively. Our study indicates that particle dynamical polydispersity as well as the structure and dynamics of dense macromolecular suspensions can be vastly tuned by internal activity in terms of internal “hot” or “cold” fluctuating states.

Computer simulations are performed to study some of the less explored aspects of the transient electrohydrodynamics of a liquid drop in uniform DC electric fields. The governing equations of the problem are solved using a parallelized front tracking/finite difference method in the framework of Taylor-Melcher's leaky dielectric theory. For density- and viscosity-matched fluid systems, the evolution of the flow field at a high Ohnesorge-squared number ${\text{Oh}}^2={\mu }^2/\gamma \rho a$ is studied. It is shown that the instantaneous flow pattern is the result of superposition of deformation- and hydrodynamic shear-driven vortices, and that depending on the placement of the fluid systems on the deformation-circulation map, there exists two different paths for the development of the velocity field toward steady state. Examination of the steady-state flow patterns shows that the location of the maximum velocity can shift from the (classically known) drop surface to inside the drop along the poles. The effect of ${\text{Oh}}^2$ on the dynamic response of the drop and the kinetic energy of the fluid is studied. For high ${\text{Oh}}^2$ number flows, the dynamic response is monotonic while the kinetic energy evolves in a nonmonotonic way, achieving a distinct peak before settling to steady state. However, for low ${\text{Oh}}^2$ number flows, both the dynamic response and kinetic energy are oscillatory. Inspection of the results show a two-way coupling between the deformation rate and the fluid flow. The effect of the density ratio $\tilde{\rho }={\rho }_i/{\rho }_o$ (drop to ambient) on the dynamic response and fluid flow strength shows that for high ${\text{Oh}}^2$ number flows both parameters remain essentially intact at steady state, while their evolution modes transition from a monotonic response to an oscillatory one at high density ratio. However, for low ${\text{Oh}}^2$ number flows, with an increase in $\tilde{\rho }$, the oscillation frequency of both parameters remain intact, while their oscillation amplitudes increase.

In this work we derive a correct expression for the one-component plasma (OCP) energy via the angular-averaged Ewald potential (AAEP). Unlike Yakub and Ronchi [J. Low Temp. Phys. 139, 633 (2005)], who had tried to obtain the same energy expression from a two-component plasma model, we used the original Ewald potential for an OCP. A constant in the AAEP was determined using the cluster expansion in the limit of weak coupling. The potential has a simple form suitable for effective numerical simulations. To demonstrate the advantages of the AAEP, we performed a number of Monte Carlo simulations for an OCP with up to a million particles in a wide range of the coupling parameter. Our computations turned out at least two orders of magnitude more effective than those with a traditional Ewald potential. A unified approach is offered for the determination of the thermodynamic limit in the whole investigated range. Our results are in good agreement with both theoretical data for a weakly coupled OCP and previous numerical simulations. We hope that the AAEP will be useful in path integral Monte Carlo simulations of the uniform electron gas.

Fluid dynamics simulations with the lattice Boltzmann method (LBM) are very memory intensive. Alongside reduction in memory footprint, significant performance benefits can be achieved by using FP32 (single) precision compared to FP64 (double) precision, especially on GPUs. Here we evaluate the possibility to use even FP16 and posit16 (half) precision for storing fluid populations, while still carrying arithmetic operations in FP32. For this, we first show that the commonly occurring number range in the LBM is a lot smaller than the FP16 number range. Based on this observation, we develop customized 16-bit formats—based on a modified IEEE-754 and on a modified posit standard—that are specifically tailored to the needs of the LBM. We then carry out an in-depth characterization of LBM accuracy for six different test systems with increasing complexity: Poiseuille flow, Taylor-Green vortices, Karman vortex streets, lid-driven cavity, a microcapsule in shear flow (utilizing the immersed-boundary method), and, finally, the impact of a raindrop (based on a volume-of-fluid approach). We find that the difference in accuracy between FP64 and FP32 is negligible in almost all cases, and that for a large number of cases even 16-bit is sufficient. Finally, we provide a detailed performance analysis of all precision levels on a large number of hardware microarchitectures and show that significant speedup is achieved with mixed FP32/16-bit.

We study the statistical properties of the off-diagonal matrix elements of observables in the energy eigenstates of integrable quantum systems. They have been found to be dense in the spin-1/2 XXZ chain, while they are sparse in noninteracting systems. We focus on the quasimomentum occupation of hard-core bosons in one dimension and show that the distributions of the off-diagonal matrix elements are well described by generalized Gamma distributions, in both the presence and absence of translational invariance but not in the presence of localization. We also show that the results obtained for the off-diagonal matrix elements of observables in the spin-1/2 XXZ model are well described by a generalized Gamma distribution.

The detection of information-bearing signals in a time series is very important for describing and analyzing a wide variety of complex physical systems. However, identifying events in low signal-to-noise ratio circumstances remains a challenge once heavy data preprocessing is usually required. In this work, we propose a robust methodology based on the instantaneous-spectral Shannon entropy for capturing microseismic events in noisy environments without the requirement of data preprocessing. We call our proposal the instantaneous spectral entropy detection (ISED) method. We tested the ISED in a couple of real empirical seismic records to detect microseismic events. Our methodology detects microseismic patterns even without any preprocessing, in contrast to usual methods in the literature which need appreciable data preprocessing.

We employ the microcanonical inflection-point analysis method, developed for the systematic identification and classification of phase transitions in systems of any size, to study the two-dimensional Ising model at various lattice sizes and in the thermodynamic limit. Exact results for the density of states, which were obtained by exact algorithmic computation, provide evidence for higher-order transitions in addition to the well-studied second-order ferromagnetic-paramagnetic phase transition. An independent third-order phase transition is identified in the ferromagnetic phase, whereas another third-order transition resides in the paramagnetic phase. The latter is a dependent transition, i.e., it is inevitably associated with the critical transition, but it remains separate from the critical point in the thermodynamic limit. For a deeper insight into the nature of these additional transitions, a detailed analysis of spin clusters is performed.

Energy equipartition can be established in short-range systems after the dynamic process of thermalization. However, energy distribution between different degrees of freedom in systems with long-range interactions is unclear. We study the dynamics of energy relaxation in the Fermi-Pasta-Ulam-Tsingou $\beta$ model with long-range quartic interactions, which decay as $1/d^{\delta }$ with $d$ being the lattice distance. The dynamic crossover of a mode-energy distribution from localized to equipartitioned with the increase of the power $\delta$ is observed. A transition of mode-energy distribution is identified around the value of $\delta =1$, which usually serves as the distinction between strong and weak long-range couplings. We elucidate that the varying frequency overlapping of the mode-energy power spectrum is responsible for this dynamic crossover. Through further calculation of the spectral entropy, the minimum duration of quasistationary states, ${\tau }_{\mathrm{QSS}}$, is found at $\delta =2$, which may provide possible dynamic explanations for the peculiar behavior of heat transport in long-range lattice chains. In addition, the double scaling in ${\tau }_{\mathrm{QSS}}$ as a function of energy density is also observed in our long-range lattices. Our results not only contribute to understanding the dynamics of energy relaxation in long-range systems, but also shed light on the longstanding problem of thermalization and low-dimensional heat transport in short-range systems.

We investigate the stochastic susceptible-infected-recovered (SIR) model of infectious disease dynamics in the Fock-space approach. In contrast to conventional SIR models based on ordinary differential equations for the subpopulation sizes of S, I, and R individuals, the stochastic SIR model is driven by a master equation governing the transition probabilities among the system's states defined by SIR occupation numbers. In the Fock-space approach the master equation is recast in the form of a real-valued Schrödinger-type equation with a second quantization Hamiltonian-like operator describing the infection and recovery processes. We find exact analytic expressions for the Hamiltonian eigenvalues for any population size $N$. We present small- and large-$N$ results for the average numbers of SIR individuals and basic reproduction number. For small $N$ we also obtain the probability distributions of SIR states, epidemic sizes and durations, which cannot be found from deterministic SIR models. Our Fock-space approach to stochastic SIR models introduces a powerful set of tools to calculate central quantities of epidemic processes, especially for relatively small populations where statistical fluctuations not captured by conventional deterministic SIR models play a crucial role.

We present experimental and numerical results for the fluctuation properties in the eigenfrequency spectra and of the scattering matrix of closed and open unidirectional quantum graphs, respectively. Unidirectional quantum graphs, that are composed of bonds connected by reflectionless vertices, were introduced by Akila and Gutkin [Akila and Gutkin, J. Phys. A: Math. Theor. 48, 345101 (2015)]. The nearest-neighbor spacing distribution of their eigenvalues was shown to comply with random-matrix theory predictions for typical chaotic systems with completely violated time-reversal invariance. The occurrence of short periodic orbits confined to a fraction of the system, that lead in conventional quantum graphs to deviations of the long-range spectral correlations from the behavior expected for typical chaotic systems, is suppressed in unidirectional ones. Therefore, we pose the question whether such graphs may serve as a more appropriate model for closed and open chaotic systems with violated time-reversal invariance than conventional ones. We compare the fluctuation properties of their eigenvalues and scattering matrix elements and observe especially in the long-range correlations larger deviations from random-matrix theory predictions for the unidirectional graphs. These are attributed to a loss of complexity of the underlying dynamic, induced by the unidirectionality.

The influence of people's individual responses to the spread of contagious phenomena, like the COVID-19 pandemic, is still not well understood. We investigate the Markovian Generalized Adaptive Susceptible-Infected-Susceptible (G-ASIS) epidemic model. The G-ASIS model comprises many contagious phenomena on networks, ranging from epidemics and information diffusion to innovation spread and human brain interactions. The connections between nodes in the G-ASIS model change adaptively over time, because nodes make decisions to create or break links based on the health state of their neighbors. Our contribution is fourfold. First, we rigorously derive the first-order and second-order mean-field approximations from the continuous-time Markov chain. Second, we illustrate that the first-order mean-field approximation fails to approximate the epidemic threshold of the Markovian G-ASIS model accurately. Third, we show that the second-order mean-field approximation is a qualitative good approximation of the Markovian G-ASIS model. Finally, we discuss the Adaptive Information Diffusion (AID) model in detail, which is contained in the G-ASIS model. We show that, similar to most other instances of the G-ASIS model, the AID model possesses a unique steady state, but that in the AID model, the convergence time toward the steady state is very large. Our theoretical results are supported by numerical simulations.

Network structure or connectivity patterns are critical in determining collective dynamics among interacting species in ecosystems. Conventional research on species persistence in spatial populations has focused on static network structure, though most real network structures change in time, forming time-varying networks. This raises the question, in metacommunities, how does the pattern of synchrony vary with temporal evolution in the network structure. The synchronous dynamics among species are known to reduce metacommunity persistence. Here we consider a time-varying metacommunity small-world network consisting of a chaotic three-species food chain oscillator in each patch or node. The rate of change in the network connectivity is determined by the natural frequency or its subharmonics of the constituent oscillator to allow sufficient time for the evolution of species in between successive rewirings. We find that over a range of coupling strengths and rewiring periods, even higher rewiring probabilities drive a network from asynchrony towards synchrony. Moreover, in networks with a small rewiring period, an increase in average degree (more connected networks) pushes the asynchronous dynamics to synchrony. On the other hand, in networks with a low average degree, a higher rewiring period drives the synchronous dynamics to asynchrony resulting in increased species persistence. Our results also follow the calculation of synchronization time and are robust across other ecosystem models. Overall, our study opens the possibility of developing temporal connectivity strategies to increase species persistence in ecological networks.

We devise a deep learning solver inspired by physics-informed neural networks (PINNs) to tackle the polymer self-consistent field theory (SCFT) equations for one-dimensional AB-diblock copolymers. The PINNs framework comprises two parallel feedforward neural networks that separately represent the segmental partition functions and self-consistent chemical potential fields. The two networks are coupled through a loss function incorporating the governing equation, initial and boundary conditions, and the incompressibility constraint. To avoid the metastable homogeneous solution, the network parameters are initialized based on known self-consistent fields obtained from the numerical pseudospectral method. For copolymers of length $N$ at a given volume fraction of A block ($f$) and the reduced Flory-Huggins interaction parameter ($\chi N$), the minimization of the loss function leads to the converged network parameters that successfully capture the stable lamellar phase. The periodicity of the lamellar structure is correctly reproduced for the explored sets of $[f,\chi N]$, irrespective of the presumed computational domain size for initialization. Moreover, the proposed PINNs are applicable to the inverse discovery of the interaction parameter and the embedded chemical potential fields for an observed structure. This capability of solving the inverse SCFT problem demonstrates the potential of using PINNs to accelerate the exploration of new polymeric materials.