Condensed Matter

Active Brownian motion with intermittent direction reversals are common in a class of bacteria like Myxococcus Xanthus and Pseudomonas putida. We show that, for such a motion in two dimensions, the presence of the two time scales set by the rotational diffusion constant D R and the reversal rate γ give rise to four distinct dynamical regimes showing distinct behaviors. We analytically compute the position distribution which shows a crossover from a strongly non-diffusive and anisotropic behavior at short-times to a diffusive isotropic behavior via an intermediate regime. We find that the marginal distribution in the intermediate regime shows an exponential or Gaussian behavior depending on whether γ is larger or smaller than D R . We also find the persistence exponents in the four regimes. In particular, we show that a novel persistence exponent α=1 emerges due to the direction reversal.

We consider the solid or hexatic non-equilibrium phases of an interacting two-dimensional system of Active Brownian Particles at high density and investigate numerically and theoretically the properties of the velocity distribution function and the associated kinetic temperature. We obtain approximate analytical predictions for the shape of the velocity distribution and find a transition from a Mexican-hat-like to a Gaussian-like distribution as the persistence time of the active force changes from the small to the large persistence regime. Through a detailed numerical and theoretical analysis of the single-particle velocity variance, we report an exact analytical expression for the kinetic temperature of dense spherical self-propelled particles that holds also in the non-equilibrium regimes with large persistence times and discuss its range of validity.

The study of thermal heat engines was pivotal to establishing the principles of equilibrium thermodynamics, with implications far wider than only engine optimization. For nonequilibrium systems, which by definition dissipate energy even at rest, how to best convert such dissipation into useful work is still largely an outstanding question, with similar potential to illuminate general physical principles. We review recent theoretical progress in studying the performances of engines operating with active matter, where particles are driven by individual self-propulsion. We distinguish two main classes, either autonomous engines exploiting a particle current, or cyclic engines applying periodic transformation to the system, and present the strategies put forward so far for optimization. We delineate the limitations of previous studies, and propose some futures perspectives, with a view to building a consistent thermodynamic framework far from equilibrium.

When the contacts of an open system flip between different reservoirs, the resulting nonequilibrium shows increased dynamical activity. We investigate such active gating for one-dimensional symmetric (SEP) and asymmetric (ASEP) exclusion models where the left/right boundary rates for entrance and exit of particles are exchanged at random times. Such rocking makes SEP spatially symmetric and on average there is no boundary driving; yet the entropy production increases in the rocking rate. For ASEP a non-monotone density profile can be obtained with particles clustering at the edges. In the totally asymmetric case, there is a bulk transition to a maximal current phase as the rocking exceeds a finite threshold, depending on the boundary rates. We study the resulting density profiles and current as functions of the rocking rate.

A crowd of nonequilibrium entities can show phase transition behaviours that are prohibited in conventional equilibrium setups. One of the simplest and well-studied phenomena observed in such active matter is the motility-induced phase separation (MIPS), where self-propelled particles spontaneously aggregate. An interesting question is whether a similar activity-driven phase transition occurs in a pure quantum system. Here we introduce a non-Hermitian quantum many-body model that undergoes quantum phase transitions, which includes the analogue of the classical MIPS within its parameter space. The phase diagram, which can be experimentally tested in an open quantum system, indicates that the addition of spin-dependent asymmetric hopping to a simple model of hard-core bosons is sufficient to induce flocking as well as phase separation. Moreover, we find that the quantum phase transitions in the model are equivalent to the transitions of dynamical paths in classical kinetics upon the application of biasing fields. This example demonstrates that quantum systems can indeed show activity-induced phase transitions, and sheds light on the rich connection between classical nonequilibrium kinetics and non-Hermitian quantum physics.

Optimizing a high-dimensional non-convex function is, in general, computationally hard and many problems of this type are hard to solve even approximately. Complexity theory characterizes the optimal approximation ratios achievable in polynomial time in the worst case. On the other hand, when the objective function is random, worst case approximation ratios are overly pessimistic. Mean field spin glasses are canonical families of random energy functions over the discrete hypercube {−1,+1 } N . The near-optima of these energy landscapes are organized according to an ultrametric tree-like structure, which enjoys a high degree of universality. Recently, a precise connection has begun to emerge between this ultrametric structure and the optimal approximation ratio achievable in polynomial time in the typical case. A new approximate message passing (AMP) algorithm has been proposed that leverages this connection. The asymptotic behavior of this algorithm has been analyzed, conditional on the nature of the solution of a certain variational problem. In this paper we describe the first implementation of this algorithm and the first numerical solution of the associated variational problem. We test our approach on two prototypical mean-field spin glasses: the Sherrington-Kirkpatrick (SK) model, and the 3 -spin Ising spin glass. We observe that the algorithm works well already at moderate sizes ( N≳1000 ) and its behavior is consistent with theoretical expectations. For the SK model it asymptotically achieves arbitrarily good approximations of the global optimum. For the 3 -spin model, it achieves a constant approximation ratio that is predicted by the theory, and it appears to beat the `threshold energy' achieved by Glauber dynamics. Finally, we observe numerically that the intermediate states generated by the algorithm have the properties of ancestor states in the ultrametric tree.

An autonomous out of equilibrium Maxwell's demon is used to reverse the natural direction of the heat flux between two electric circuits kept at different temperatures and coupled by the electric thermal noise. The demon does not process any information, but it achieves its goal by using a frequency dependent coupling with the two reservoirs of the system. There is no energy flux between the demon and the system, but the total entropy production (system+demon) is positive. The demon can be power supplied by thermocouples. The system and the demon are ruled by equations similar to those of two coupled Brownian particles and of the Brownian gyrator. Thus our results pave the way to the application of autonomous out equilibrium Maxwell demons to coupled nanosystems at different temperatures.

We investigate the critical behavior of the two-dimensional spin- 1 Baxter-Wu model in a crystal field using entropic sampling simulations with the joint density of states. We obtain the temperature-crystal field phase diagram, which includes a tetracritical line ending at a pentacritical point. A finite-size scaling analysis of the maximum of the specific heat, while changing the crystal field anisotropy, is used to obtain a precise location of the pentacritical point. Our results give the critical temperature and crystal field as T pc =0.98030(10) and D pc =1.68288(62) . We also detect that at the first-order region of the phase diagram, the specific heat exhibits a double peak structure as in the Schottky-like anomaly, which is associated with an order-disorder transition.

The aim of this focus letter is to present a comprehensive classification of the main entropic forms introduced in the last fifty years in the framework of statistical physics and information theory. Most of them can be grouped into three families, characterized by two-deformation parameters, introduced respectively by Sharma, Taneja, and Mittal (entropies of degree (α,β )), by Sharma and Mittal (entropies of order (α,β) ), and by Hanel and Thurner (entropies of class (c,d) ). Many entropic forms examined will be characterized systematically by means of important concepts such as their axiomatic foundations {\em ? la} Shannon-Khinchin and the consequent composability rule for statistically independent systems. Other critical aspects related to the Lesche stability of information measures and their consistency with the Shore-Johnson axioms will be briefly discussed on a general ground.

Exponential random graphs are important to model the structure of real-world complex networks. Here we solve the two-star model with degree-degree correlations in the sparse regime. The model constraints the average correlation between the degrees of adjacent nodes (nearest neighbors) and between the degrees at the end-points of two-stars (next nearest neighbors). We compute exactly the network free energy and show that this model undergoes a first-order transition to a condensed phase. For non-negative degree correlations between next nearest neighbors, the degree distribution inside the condensed phase has a single peak at the largest degree, while for negative degree correlations between next nearest neighbors the condensed phase is characterized by a bimodal degree distribution. We calculate the degree assortativities and show they are non-monotonic functions of the model parameters, with a discontinuous behavior at the first-order transition. The first-order critical line terminates at a second-order critical point, whose location in the phase diagram can be accurately determined. Our results can help to develop more detailed models of complex networks with correlated degrees.