Physical Review E最新文献

We numerically investigate the transport behavior of multiple deformable particles in time-oscillating potentials. For a fixed potential asymmetry, the transport direction is determined by the competition between two nonequilibrium driving mechanisms: the self-propulsion speed and the oscillation frequency of the potential. Particle deformability can either enhance or impede transport depending on which driving force dominates. Both rotational noise and particle density exhibit nonmonotonic influences, including velocity reversals. By carefully tuning system parameters, multiple reversals of the average particle velocity can be achieved, providing a potential mechanism for selective particle separation. Compared to single-particle systems, collective interactions give rise to richer dynamics and stronger transport rectification. These findings deepen the theoretical understanding of active soft matter in time-dependent potentials and may guide the design of experimental strategies for controlling and separating deformable particles in complex environments.

Predicting extreme events is a challenging task due to their occasional appearance at irregular time intervals and with sudden large amplitudes. In particular, accurate forecasting of both the amplitude and timing of occurrence is difficult. We make an attempt to address the challenges using reservoir computing machine learning based on partial or complete information of the system variables in a few paradigmatic dynamical systems, namely, the forced Liénard system, the coupled FitzHugh-Nagumo model, and a model of hidden attractor. The efficacy of the machine learning approach has been tested using numerically generated data using models and a real-time experiment. The machine is able to successfully predict the transition points to extreme events via two well-known nonlinear processes, namely, Pomeau-Manneville intermittency and crisis-induced intermittency (interior crisis) against a system parameter by reconstruction of the bifurcation diagram by training the machine with simulated data. The machine is able to retrace the attractors of the systems quite efficiently and also reproduce the distributions of events and interevent intervals, thereby preserving the statistical properties of extreme events in the model systems. Prediction of time evolution is still limited to the length of a few Lyapunov times before it diverges from the original state as expected due to the complex nature of the extreme event dynamics. The amplitude of extreme events in the time series is preserved or predicted almost accurately. A prediction horizon metric is used for measuring the success level of the machine and deciding the minimal number of inputs necessary for efficient learning and prediction of the forced Liénard system and the hidden attractor model. For the coupled FitzHugh-Nagumo model, we use the mean square error measure to find an appropriate choice of a pair of inputs that is necessary for successful prediction. Using experimental data from an analog forced Liénard circuit, so far the machine could learn the dynamics and the long-term statistics of extreme events originating via Pomeau-Manneville intermittency.

Under high cylindrical confinement, two athermal ring polymers segregate to two halves of the cylinder to maximize their entropy. This has been identified as the primary mechanism of chromosome segregation in cylindrical E. coli cells. In contrast, chromosomes in eukaryotic cells are confined in a spherical nucleus. As ring polymers will remain mixed within spherical confinement, in this work, we provide a simple mechanism to tune entropic interactions and drive organization within the sphere by creating asymmetric topological modifications in the ring-polymer architecture. We introduced cross-links between specific monomers on the ring polymer contour to create a cluster of internal loops connected to a bigger loop. Consequently, we observed an emergent radial organization of the polymer segments in the sphere. For a single topologically modified ring polymer within a sphere, the monomers of the bigger loop were probabilistically found closer to the periphery. However, for multiple such polymers in the sphere, the small loops were localized near the periphery. We considered the bead-spring model of polymers, where there are only repulsive excluded volume interactions between the monomers, ensuring that the observed organization is purely entropy-driven. We also observe a similar organization when we allow topological constraint release by allowing chains to cross each other, as is relevant for chromosome physics. This leads us to a separate investigation where we infer that excluded volume interactions between beads are enough to give a Flory exponent of 0.6, even if we allow linear polymeric chains to cross each other. Finally, we discuss the plausible relevance of our studies to the organization of euchromatin and the more condensed heterochromatin in eukaryotic chromosomes.

In a mix of prejudiced and unprejudiced individuals engaged in strategic interactions, the individual intensity of prejudice is expected to influence the overall level of societal prejudice. A high level of prejudice should lead to discrimination that may manifest as unfairness and, perhaps, even spite. In this paper, we investigate this idea in the classical paradigm of the ultimatum game, which we theoretically modify to introduce prejudice at the level of players, terming its intensity as prejudicity. The stochastic evolutionary game dynamics, in the regime of replication-selection, reveals the emergence of spiteful behavior as a dominant behavior via a first-order phase transition-a discontinuous jump in the frequency of spiteful individuals at a threshold value of prejudicity. The phase transition is quite robust and becomes progressively conspicuous in the limit of large population size, where deterministic evolutionary game dynamics, viz., replicator dynamics, approximates the system closely. The emergence of spite driven by prejudice is also found to persist when one considers long-term evolutionary dynamics in the mutation-selection dominated regime.

We investigate the coevolution of network structure and opinion dynamics by integrating a threshold-based complex contagion model with a target rewiring mechanism. In contrast to previous models that allow all nonadopting nodes to rewire indiscriminately, our framework introduces the concept of superspreader nonadopting nodes that are particularly instrumental in spreading the adoption. Only these nodes rewire their connections away from adopting neighbors to randomly chosen nonadopting nodes. Through mean-field theoretical analysis and numerical simulations, we show that this targeted rewiring strategy efficiently contains adoption cascades while significantly reducing the overall number of rewiring operations required. The presence of superspreaders naturally localizes structural adaptation to critical points in the network, leading to a more economical and stable network evolution. Our results reveal that this mechanism not only substantially reduces the cost of rewiring but also causes minimal structural change, making the system more efficient and realistic in terms of intervention cost and rewiring burden.

We design a thermal bath that preserves the conservation of a system's angular momentum or allows it to fluctuate around a specified nonzero mean while maintaining a Boltzmann distribution of energy in the steady state. We demonstrate that classical particles immersed in such baths exhibit position-momentum uncertainties with a strictly positive lower bound proportional to the absolute value of the mean angular momentum. The proportionality constant, c, is dimensionless and does not depend explicitly on the system's parameters. Remarkably, while c is universally bounded by unity, it attains the exact value c=1/2 for particles in central potentials.

The linear or nonlinear nature of a dynamical system is often evaluated by applying nonlinearity tests to an experimental time series that is typically the only observable output of the system. The most widely used nonlinearity test is the method of surrogates: a set of linear time series that replicate the properties of the experimental time series, specifically the marginal distribution and the autocorrelation function. This set of surrogates represents the null hypothesis of linearity, and can be created by several techniques. However, these techniques typically require some degree of manipulation in the frequency domain that often produces correlations in the Fourier phases leading to undesired nonlinear correlations in the surrogates. Here, we present a nonlinearity test that does not rely on generating surrogates, therefore avoiding the problem of spurious nonlinearities in the null hypothesis. Our approach uses the autocorrelation function of the time series under assessment to statistically determine whether the observed correlations could arise from a linear Gaussian time series that has been reversibly transformed into the experimental time series. If so, the experimental time series is considered linear; if not, it is considered nonlinear. We have applied the test to several well known models of linear and nonlinear time series, obtaining excellent results in both cases.

The so-called eigenstate thermalization hypothesis (ETH), which has been tested in various many-body models by numerical simulations, supplies a way of understanding eventual thermalization and is believed to be important for understanding processes of thermalization. Two functions play important roles in the application of ETH, one for averaged diagonal elements and the other for the variance of off-diagonal elements of an observable addressed by ETH on the energy basis. For the former function, a semiclassical expression is known of the zeroth order of ℏ, while, little is known analytically for the latter. In this paper, a semiclassical expression is derived for the former diagonal function, which includes higher-order contributions of ℏ. And, another semiclassical expression is derived for the scale of the height of a central platform of the latter off-diagonal function. These analytical results are checked numerically in the Lipkin-Meshkov-Glick model. Possible relevance of the latter result for future investigation is discussed.

We present a general framework for determining the power-efficiency trade-off relations across arbitrary thermal machines, addressing the lack of unified optimization results stemming from their diverse functionalities (e.g., heat engines, refrigerators, and heat pumps). For time-dependent cycle irreversibility A(τ) following a τ^{-α} power law, where α is an interaction-dependent parameter, we show that engineering the interactions between thermal machines and reservoirs enables control over the trade-off relations, with the efficiency at maximum power approaching Carnot efficiency as α increases. Setting α=1 naturally recovers typical low-dissipation regime results. Additionally, we derive the first power-efficiency trade-off for finite-time quantum adiabatic Otto machines with τ^{-2}-scaling. This work establishes a unified constraint for thermodynamic cycles across nonequilibrium regimes, facilitating consistent optimization of diverse thermal devices in practice.

Machine-learned interatomic potentials (MLIPs) are rapidly becoming the standard for atomistic simulations, but their application to magnetic materials remains challenging because spin fluctuations must be treated either explicitly or implicitly. Here, we investigate this problem for the technologically important Fe-Cr-C system by constructing two deep machine learning potentials within the DeePMD framework: one trained on nonmagnetic DFT data (DP-NM) and one on spin-polarized DFT data (DP-M). Extensive validation against experiments reveals that DP-NM accurately reproduces dynamic, collective properties such as viscosity and melting temperatures, while DP-M excels in describing static, local properties such as density, especially for Fe-rich alloys. We rationalize these findings by noting that, at high temperatures, local magnetic moments self-average over space and time, making explicit spin treatment unnecessary for transport properties but essential for equilibrium volumes. To mitigate the high cost of spin-polarized DFT sampling, we employ a transfer-learning strategy, pretraining on nonmagnetic data followed by fine-tuning on a small spin-polarized dataset, which reduces computational expense by more than an order of magnitude. Furthermore, we benchmark several state-of-the-art foundation models: MACE, GRACE, DPA3 and fine-tuned variants of the latter against our specialized potentials. We find that foundation models offer competitive accuracy after fine-tuning but remain significantly slower in molecular dynamics simulations, limiting their practicality for large-scale transport property calculations. Our results establish clear design principles for MLIPs targeting magnetic alloys: (i) nonmagnetic training data suffice for dynamic properties of paramagnetic melts, and (ii) spin-polarized training is essential only for precise static properties of ferromagnetic phases. These insights provide a roadmap for efficient development of transferable MLIPs for magnetic systems and clarify the role of foundation models and transfer learning in accelerating this process.