Dissipation bounds the amplification of transition rates far from equilibrium

Abstract

Complex systems can convert energy imparted by nonequilibrium forces to regulate how quickly they transition between long-lived states. While such behavior is ubiquitous in natural and synthetic systems, currently there is no general framework to relate the enhancement of a transition rate to the energy dissipated or to bound the enhancement achievable for a given energy expenditure. We employ recent advances in stochastic thermodynamics to build such a framework, which can be used to gain mechanistic insight into transitions far from equilibrium. We show that under general conditions, there is a basic speed limit relating the typical excess heat dissipated throughout a transition and the rate amplification achievable. We illustrate this tradeoff in canonical examples of diffusive barrier crossings in systems driven with autonomous and deterministic external forcing protocols. In both cases, we find that our speed limit tightly constrains the rate enhancement.

Phase Diagram of Active Brownian Spheres: Crystallization and the Metastability of Motility-Induced Phase Separation

“…Homogeneous nucleation and stability.-Despite recent progress in the development of importance sampling techniques for nonequilibrium systems [64][65][66][67][68][69][70][71][72][73], the ability to comprehensively survey the phase behavior of many-particle active systems [74][75][76][77] remains limited. In the absence of these tools, we make an appeal to two-state rate theory to identify the relative stability of the two coexistence scenarios.…”

Reinforcement learning of rare diffusive dynamics

“…In molecular systems, rare events determine the rates by which chemical reactions occur and phases interconvert, 5 and they also encode the response of systems driven to flow or unfold. [6][7][8][9][10] Strategies that afford a means of studying rare dynamical events in statistically unbiased ways are particularly desired, in order to deduce the intrinsic pathways by which they occur and to evaluate their likelihoods. Borrowing notions from reinforcement learning, 11 we have developed a method to generate rare dynamical trajectories directly through the optimization of an auxiliary dynamics that generates an ensemble of trajectories with the correct relative statistical weights.…”

Principles of Low Dissipation Computing from a Stochastic Circuit Model

“…For example, dissipation bounds the rate at which a system transforms between different states. [22][23][24][25][26] Dissipation also provides an upper bound for the precision of a current. [27][28][29] A universal tradeoff between power, precision and speed has been proposed for communication systems as well.…”