Recombination effects are shown to modify the charge-state distribution achieved in the acceleration of heavy ions during high-intensity laser-solid interactions. A recombination package has been developed for particle-in-cell codes, which includes dielectronic, radiative, and three-body recombination. One-dimensional simulations were then performed to model a recent experiment, where $4.1\times {10}^{20}\text{W}{\text{cm}}^{-2}$ laser pulses heated thin gold targets to produce gold ion beams. Simulations show that the ion beams from 100-nm targets were unaffected by recombination, but that recombination caused charge states to drop in 300-nm targets, similar to experimental observations. These differences were attributed to target temperature, as 300-nm targets were heated to lower temperatures than 100-nm targets during the laser-target interaction, causing ions to spend more time in environments where recombination rates dominated ionization rates.

Ising machines are considered as promising hardware solvers. The idea is to map the solution of a problem to the ground state of an Ising spin glass and find this minimum by performing annealing in an Ising machine that emulates spin physics. However, previous approaches to deterministic sequential algorithmics have been plagued by the hiearchy problem: couplings become exponentially small along the causal direction. Here, we present a method to map sequential algorithms to an Ising problem with only a linear decay of the couplings and illustrate this with $n$-bit binary number adders. The resulting computing system is thus scalable, opening a way for efficient Ising machines for sequential algorithms.

This work presents a lattice Boltzmann (LB) model for immiscible multiphase flow with phase change. By integrating the pseudopotential model with the color-gradient model, the current model is capable of capturing phase change spontaneously during the immiscible multiphase flows for the first time, with corrected interaction forces at interfaces. The proposed model is comprehensively verified via multiple benchmark cases and applied to investigate the phase change effects on immiscible multiphase flow and displacement in porous media. Our simulation results reveal mechanisms from the gas blockage effects at different capillary numbers and the compressibility effect during immiscible multiphase displacement with phase change in porous media. The pronounced disparity between models that incorporate phase change or not underscores the critical importance of considering phase transitions in immiscible multiphase flow systems.

This study investigates the phase separation kinetics of binary ($AB$) polymer blends under various shear conditions using dissipative particle dynamics simulations. The system, confined by two parallel walls, is subjected to different shear cases, including stationary and moving walls. The simulations explore critical and off-critical polymer compositions, analyzing morphological evolution, scaling behavior, domain growth, and structural anisotropy. Our results indicate shear-induced alignment and thinning of polymer domains. The characteristic length scales exhibit power-law growth: $R\left(t\right)\sim t^{\varphi }$, where $\varphi$ is the effective growth exponent. Initially, the system displays viscous hydrodynamic growth with an exponent of $\varphi \sim 1$. As time progresses, this transitions to inertial hydrodynamic growth characterized by $\varphi \sim 2/3$. Additionally, for off-critical mixtures, the inertial growth exponent slightly reduces as the shear rate increases. Higher shear rates result in anisotropic structures, such as deformed cylindrical domains in off-critical blends and distorted lamellae in critical mixtures. The findings provide insights into manipulating polymer morphologies under shear, offering pathways for industrial applications in materials design and processing.

Systems with a complex dynamics like glasses or models of biological evolution are often pictured in terms of a complex landscape, with a large number of possible collective states. We show on the example of a stochastic spin model with nonreciprocal and heterogeneous interactions how the complex landscape picture can be generalized far from equilibrium, where collective states may become time-dependent and exhibit, e.g., spontaneous oscillations, often hidden by the presence of disorder. We identify relevant observables, like the density of entropy production rate, to unveil the spontaneous collective time dependence, and we determine a configurational entropy which counts the number of oscillating collective states when this number grows exponentially with system size.

We present a quantum-heat engine model utilizing a dual spin-pair architecture, wherein an Otto-like cycle is implemented using a single heat bath. The conventional cold bath is replaced by a measurement protocol, enabling engine operation without the need for a second thermal reservoir. Unlike standard quantum-heat engines, our framework employs an ancillary spin pair in a two-dimensional configuration to regulate performance. Operating in finite time, the engine attains finite power, which is enhanced through quantum correlations, specifically correlation between spin pairs and projective measurements on the ancillary pair. The system consists of dual-qubit pairs, where one pair serves as the working medium and the other as an ancillary system facilitating measurement-induced heat exchange. We demonstrate that the engine efficiency can exceed the standard quantum Otto limit through local control of the ancillary pair while maintaining nonzero power output. Moreover, correlation between spin pairs enables efficiency modulation via the measurement basis, underscoring the role of quantum resources in optimizing quantum thermal machines.

In this study, we propose an optomechanical cavity as a promising platform for charging a quantum battery embedding two two-level atoms. We assume the atoms and the single optical mode are initialized in their corresponding ground states while the mechanical resonator can be either in vacuum or its first excited state. Our findings reveal a remarkable correspondence between the generated entanglement between the atoms and the ergotropy of the quantum battery, i.e., the available energy by unitary operations. This observation of such a high degree of similarity between entanglement and ergotropy seems to be unprecedented. Furthermore, both linear and nonlinear optomechanical couplings are shown to enhance ergotropy provided being properly adjusted. Additionally, we examine the statistical properties of the optical and mechanical modes using the Mandel parameter. Our results indicate that the nonclassicality of the optical mode significantly boosts the available energy. In contrast, less negative Mandel parameter values, reflecting decreased nonclassicality, are preferable to assist the charging process. These insights suggest the possibility of using quantum features to optimize the efficiency of quantum batteries.

Behavioral experiments on the ultimatum game (UG) reveal that we humans prefer fair acts, which contradicts the prediction made in orthodox economics. Existing explanations, however, are mostly attributed to exogenous factors within the imitation learning framework. Here, we adopt the reinforcement learning paradigm, where individuals make their moves aiming to maximize their accumulated rewards. Specifically, we apply $Q$ learning to UG, where each player is assigned two $Q$ tables to guide decisions for the roles of proposer and responder. In a two-player scenario, fairness emerges prominently when both experiences and future rewards are appreciated. In particular, the probability of successful deals increases with higher offers, which aligns with observations in behavioral experiments. Our mechanism analysis reveals that the system undergoes two phases, eventually stabilizing into fair or rational strategies. These results are robust when the rotating role assignment is replaced by a random or fixed manner, or the scenario is extended to a latticed population. Our findings thus conclude that the endogenous incentive is sufficient to explain the emergence of fairness, exogenous factors are unnecessary.

In Bayesian inference, computing the posterior distribution from the data is typically a nontrivial problem, which usually requires approximations such as mean-field approaches or numerical methods, like the Monte Carlo Markov chain. Being a high-dimensional distribution over a set of correlated variables, the posterior distribution can undergo the notorious replica symmetry-breaking transition. When that happens, several mean-field methods and virtually every Monte Carlo scheme cannot provide a reasonable approximation to the posterior and its marginals. Replica symmetry is believed to be guaranteed whenever the data are generated with known prior and likelihood distributions, namely under the so-called Nishimori conditions. In this paper, we break this belief by providing a counterexample showing that under the Nishimori conditions, replica symmetry breaking arises. Introducing a simple, geometrical model that can be thought of as a patient-zero retrieval problem in a highly infectious regime of the epidemic Susceptible-Infectious model, we show that under the Nishimori conditions there is evidence of replica symmetry breaking. We achieve this result by computing the instability of the replica symmetric cavity method toward the one-step replica symmetry-broken phase. The origin of this phenomenon—replica symmetry breaking under the Nishimori conditions—is likely due to the correlated disorder appearing in the epidemic models.

Electrostatic discharges occur in numerous media on a range of length scales, from microscopic discharges inside electronic materials to kilometer-long channels in air during natural lightning events. To study the mechanisms and behavior of electrostatic discharges inside materials, we measured the discharge currents produced during dielectric breakdown of electron-charged polymethyl methacrylate. The resulting current waveforms were analyzed and the relevant parameters for characterizing the waveform morphology were extracted and investigated to build a functional model to describe and characterize the waveforms from these types of discharges. The model involves a distinction between one-dimensional and two-dimensional discharge channel growth, which are governed by separate principles and thus have different contributions to the total measured discharge current. We show that this model, which is derived from the discharge channel growth during the event, allows us to infer characteristics about the dynamics of the breakdown, such as the instantaneous velocity of the channels during breakdown. We applied this model to the context of natural compact intracloud discharges, or narrow bipolar events, by scaling the one-dimensional waveforms in size and time to those relevant to compact intracloud discharges. We find that the discharge currents derived from centimeter-scale and submicrosecond-duration electrostatic discharge events in electron-irradiated polymethyl methacrylate obey the same functional form of those of natural compact intracloud discharges, leading to the conclusion that the polymethyl methacrylate– (PMMA) based discharges may a useful laboratory tool to reproduce narrow bipolar events discharge currents. We calculated the electric field that would be radiated by these scaled discharge currents using published measurement parameters for compact intracloud discharges and found that the magnitude and pulse shape agree with observations very well. In addition, we find that energy spectral density of the scaled PMMA discharge waveforms have a similar frequency dependence in the 3- to 300-MHz band to those of compact intracloud discharges. These results support the idea that space charge in and around thunderclouds may be the driving mechanism of compact intracloud discharges and that electron-irradiated solid dielectrics may be a useful laboratory-based analog to study natural lightning events.

Experiments at the U.S. National Ignition Facility (NIF) [Döppner et al., Nature (London) 618, 270 (2023)] have created highly compressed hot hydrogenlike Be plasmas. Published analyses of the the NIF experiment have used finite-$T$ multiatom density-functional theory with Molecular dynamics, and Path-Integral Monte Carlo simulations. These methods are very expensive to implement and often lack physical transparency. Here we (i) relate their results to simpler first-principles average-atom results, (ii) establish the feasibility of rapid data analysis, with good accuracy and gain in physical transparency, and (iii) show that the NIF experiment reveals high-$T$ spin-singlet pairing of hydrogenlike Be ions with near neighbors. Our analysis predicts such stabilization over a wide range of compressed densities for temperatures close to two million Kelvin. Calculations of structure factors $S\left(k\right)$ for electrons or ions, the Raleigh weight and other quantities of interest to x-ray Thomson scattering are presented. We find that the NIF data at the scattering wave vector $k_{sc}$ of $7.89{\AA }^{-1}$ are more consistent with a density of $20\pm 2\mathrm{g}/{\mathrm{cm}}^3$, mean ionization $\overline{Z}=3.25$, at a temperature of $\simeq$ 1 800 000 K than the $34\mathrm{g}/{\mathrm{cm}}^3,\overline{Z}=3.4$ proposed by the NIF team. The relevance of ion-electron coupled-modes in studying small $k_{sc}$ data is indicated.

Mesoscopic active systems exhibit various unique behaviors—absent in passive systems—due to the forces generated by the corresponding constituents by converting their available free energies. However, estimating these forces—which are also stochastic and remain intertwined with the thermal noise—is especially nontrivial. Here, we introduce a technique to extract such fluctuating active forces acting on a passive particle immersed in an active bath with high statistical accuracy by filtering out the related thermal noise. We first test the efficacy of our method under numerical scenarios with different types of activity, and then apply it to the experimental trajectories of a microscopic particle (optically) trapped inside an active bath consisting of motile E. coli bacteria. We believe that our simple yet powerful approach, which appears agnostic to the nature of the active force, should enable accurate measurement of force dynamics in living matter and potentially allow direct but reliable estimation of key thermodynamic parameters, such as heat, work, and entropy production.

Using the Metropolis algorithm, we simulate the relaxation process of the three-dimensional kinetic Ising model. Starting from a random initial configuration, we first present the average equilibration time across the entire phase boundary. It is observed that the average equilibration time increases significantly as the temperature decreases far from the critical temperature $T_{\mathrm{c}}$. The average equilibration time along the first-order phase transition (1st-PT) line exhibits an ultraslow relaxation. We also investigate the dynamic scaling behavior with system sizes, and find that dynamic scaling holds not only near $T_{\mathrm{c}}$, but also at $T\ll T_{\mathrm{c}}$. The dynamic exponent at $T\ll T_{\mathrm{c}}$ is larger than that near $T_{\mathrm{c}}$. Additionally, we analyze the dynamic scaling of the average autocorrelation time and find that it depends on system size only near $T_{\mathrm{c}}$, while it becomes size-independent both above and below $T_{\mathrm{c}}$. The extremely slow relaxation dynamics observed near the 1st-PT is attributed to the complex structure of the free energy.

Two numerical methods are proposed to detect and analyze coupling in time series from deterministic nonlinear systems. The first identifies the presence of coupling or interdependence, while the second determines the directionality of coupling. The second method can also identify latent coupling—series that are not directly coupled but are correlated due to influence by another, unobserved system. Both methods accommodate periodic, aperiodic, and chaotic dynamics, and use order statistics derived from relative distances within a time-delay embedding. The methods are intended to be practical and apply to data sets consisting of simultaneous system recordings, and robust to observational noise due to their use of order statistics.

The free-energy-based lattice Boltzmann method has emerged as a reliable tool for simulating multiphase flows, but its application to small droplet interactions with surrounding flows is hindered by the issue of spontaneous droplet shrinkage. In contrast to previous analyses based on phase-field theory, we investigate spontaneous droplet shrinkage within the framework of the lattice Boltzmann equation for a free-energy-based model and identify that local density continues to evolve even after the system reaches equilibrium. To address this undesired density evolution, we introduce a mass correction formulated as a Laplacian term, ensuring that local mass conservation is improved without sabotaging global mass conservation. Numerical tests in several three-dimensional scenarios demonstrate that the proposed mass correction effectively mitigates droplet shrinkage and maintains droplet volume, without adversely affecting other aspects of the simulation, such as numerical stability, mass conservation, and flow velocity evolution.

It has been shown that an anisotropic liquid crystalline (LC) environment can be used to guide the self-propulsion dynamics of dispersed microswimmers, such as bacteria. This type of composite system is named “living nematic.” In the dilute limit, bacteria are found to mainly follow the local director field. Beyond the dilute limit, however, they exhibit novel dynamical behaviors, from swirling around a spiral $+1$ defect pattern to forming undulating waves, and to active turbulence. Our current knowledge of how these different behaviors emerge at different population densities remains limited. Here, we develop a hybrid method to simulate the dynamics of microswimmers dispersed in a nematic LC. Specifically, we model the microswimmers as active Brownian particles whose dynamics are coupled to a hydrodynamic model of nematic LCs to describe the evolution of the flow field and the LC structure. Our method is validated across a wide range of microswimmer populations by comparing to existing quasi-two-dimensional (2D) experiments, including undulated swirling around a spiral $+1$ defect pattern and stabilized undulated jets on a periodic C-pattern. We further extend our method to three-dimensional (3D) systems by examining loop-defect dynamics. We find that the morphodynamics and destiny of a loop defect not only depend on the activity (self-propulsion velocity), effective size, and the initial distribution of the swimmers, but also rely on its winding profile. For a wedge-twist loop defect, its dynamics are mainly determined by the position and orientation of the $+1/2$ wedge. For a pure-twist loop defect, radial twist windings play a similar role as the $+1/2$ wedge in the wedge-twist loop defect, while other windings can engender out-of-plane active flows to buckle the pure-twist loop. Finally, we consider the stochastic reversals of the self-propulsion direction of the microswimmers. By varying the characteristic reversal time, we predict that microswimmers do not necessarily tend to accumulate on splay regions. Taken together, our hybrid method provides a faithful tool to explain and guide the experiments of living nematics in both 2D and 3D, sheds light on the interplay between microswimmer distribution and defect dynamics, and unravels the design principles of using LCs to control active matter.

We consider a generalization of the well-known SEIR model of epidemic spreading with two susceptible compartments and without permanent immunity. It is shown that the set of ordinary differential equations defining its dynamics can be cast in a single one in the form of an Abel's differential equation of the second kind. While a general solution of this equation is not known, we obtain exact analytical expressions for the important short-term growth and long-term decay regimes of the epidemic outbreak. In particular, exact expressions for the exponential growth and decay rates and the exact relation between the growth rate and the basic reproduction number are obtained. Finally, we discuss the relevance of the initial conditions in these type of models and show an application to the Omicron wave of the Covid-19 pandemic in different countries.

We propose an approximation for the first return time distribution of random walks on undirected networks. We combine a message-passing solution with a mean-field approximation to account for the short- and long-term behaviors, respectively. We test this approximation on several classes of large graphs and find excellent agreement between our approximations and the true distributions. While the statistical properties of a random walk will depend on the structure of the network, the observed agreement between our approximations and numerical calculations implies that while local structure is clearly very influential, global structure is only important in a relatively superficial way, namely through the total number of edges.