Emergent second law for non-equilibrium steady states

Abstract

The Gibbs distribution universally characterizes states of thermal equilibrium. In order to extend the Gibbs distribution to non-equilibrium steady states, one must relate the self-information {{{{{{{mathcal{I}}}}}}}}(x)=-!log ({P}_{{{{{{{{rm{ss}}}}}}}}}(x)) of microstate x to measurable physical quantities. This is a central problem in non-equilibrium statistical physics. By considering open systems described by stochastic dynamics which become deterministic in the macroscopic limit, we show that changes {{Delta }}{{{{{{{mathcal{I}}}}}}}}={{{{{{{mathcal{I}}}}}}}}({x}_{t})-{{{{{{{mathcal{I}}}}}}}}({x}_{0}) in steady state self-information along deterministic trajectories can be bounded by the macroscopic entropy production Σ. This bound takes the form of an emergent second law {{Sigma }}+{k}_{b}{{Delta }}{{{{{{{mathcal{I}}}}}}}},ge ,0, which contains the usual second law Σ ≥ 0 as a corollary, and is saturated in the linear regime close to equilibrium. We thus obtain a tighter version of the second law of thermodynamics that provides a link between the deterministic relaxation of a system and the non-equilibrium fluctuations at steady state. In addition to its fundamental value, our result leads to novel methods for computing non-equilibrium distributions, providing a deterministic alternative to Gillespie simulations or spectral methods.