The distribution function of entropy flow in stochastic systems

We obtain a simple direct derivation of the differential equation governing the entropy flow probability distribution function of a stochastic system first obtained by Lebowitz and Spohn. Its solution agrees well with the experimental results of Tietz et al [2006 {\it Phys. Rev. Lett.} {\bf 97} 050602]. A trajectory-sampling algorithm allowing to evaluate the entropy flow distribution function is introduced and discussed. This algorithm turns out to be effective at finite times and in the case of time-dependent transition rates, and is successfully applied to an asymmetric simple exclusion process

Work probability distribution in systems driven out of equilibrium

We derive the differential equation describing the time evolution of the work probability distribution function of a stochastic system which is driven out of equilibrium by the manipulation of a parameter. We consider both systems described by their microscopic state or by a collective variable which identifies a quasiequilibrium state. We show that the work probability distribution can be represented by a path integral, which is dominated by ``classical'' paths in the large system size limit. We compare these results with simulated manipulation of mean-field systems. We discuss the range of applicability of the Jarzynski equality for evaluating the system free energy using these out-of-equilibrium manipulations. Large fluctuations in the work and the shape of the work distribution tails are also discussed

Evaluation of free energy landscapes from manipulation experiments

A fluctuation relation, which is an extended form of the Jarzynski equality, is introduced and discussed. We show how to apply this relation in order to evaluate the free energy landscape of simple systems. These systems are manipulated by varying the external field coupled with a systems' internal characteristic variable. Two different manipulation protocols are here considered: in the first case the external field is a linear function of time, in the second case it is a periodic function of time. While for simple mean field systems both the linear protocol and the oscillatory protocol provide a reliable estimate of the free energy landscape, for a simple model ofhomopolymer the oscillatory protocol turns out to be not reliable for this purpose. We then discuss the possibility of application of the method here presented to evaluate the free energy landscape of real systems, and the practical limitations that one can face in the realization of an experimental set-up

Efficiency of molecular machines with continuous phase space

We consider a molecular machine described as a Brownian particle diffusing in a tilted periodic potential. We evaluate the absorbed and released power of the machine as a function of the applied molecular and chemical forces, by using the fact that the times for completing a cycle in the forward and the backward direction have the same distribution, and that the ratio of the corresponding splitting probabilities can be simply expressed as a function of the applied force. We explicitly evaluate the efficiency at maximum power for a simple sawtooth potential. We also obtain the efficiency at maximum power for a broad class of 2-D models of a Brownian machine and find that loosely coupled machines operate with a smaller efficiency at maximum power than their strongly coupled counterparts.Comment: To appear in EP

Proportion of Unaffected Sites in a Reaction-Diffusion Process

We consider the probability $P(t)$ that a given site remains unvisited by any of a set of random walkers in $d$ dimensions undergoing the reaction $A+A\to0$ when they meet. We find that asymptotically $P(t)\sim t^{-\theta}$ with a universal exponent \theta=\ffrac12-O(\epsilon) for $d=2-\epsilon$, while, for $d>2$, $\theta$ is non-universal and depends on the reaction rate. The analysis, which uses field-theoretic renormalisation group methods, is also applied to the reaction $kA\to0$ with $k>2$. In this case, a stretched exponential behaviour is found for all $d\geq1$, except in the case $k=3$, $d=1$, where P(t)\sim {\rm e}^{-\const (\ln t)^{3/2}}.Comment: 10 pages, (revised version with abstract included) OUTP-94-35