Riemannian Geometry of Optimal Driving and Thermodynamic Length and its Application to Chemical Reaction Networks

Abstract

It is known that the trajectory of an endoreversibly driven system with minimal dissipation is a geodesic on the equilibrium state space. Thereby, the state space is equipped with the Riemannian metric given by the Hessian of the free energy function, known as Fisher information metric. However, the derivations given until now require both the system and the driving reservoir to be in local equilibrium. In the present work, we rederive the framework for chemical reaction networks and thereby enhance its scope of applicability to the nonequilibrium situation. Moreover, because our results are derived without restrictive assumptions, we are able to discuss phenomena that could not been seen previously. We introduce a suitable weighted Fisher information metric on the space of chemical concentrations and show that it characterizes the dissipation caused by diffusive driving, with arbitrary diffusion rate constants. This allows us to consider driving far from equilibrium. As the main result, we show that the isometric embedding of a steady state manifold into the concentration space yields a lower bound for the dissipation when the system is driven along the manifold. We give an analytic expression for this bound and for the corresponding geodesic, and thereby are able to dissect the contributions from the driving kinetics and from thermodynamics. Finally, we discuss in detail the application to quasi-thermostatic steady states.

13 pages, 5 figures

References in corpus (8)

• Stochastic thermodynamics of chemical reaction networks
• The geometry of thermodynamic control
• Irreversible thermodynamics of open chemical networks I: Emergent cycles and broken conservation laws
• Thermodynamic uncertainty relation and thermodynamic speed limit in deterministic chemical reaction networks
• Law of localization in chemical reaction networks
• Information Geometric Inequalities of Chemical Thermodynamics
• Structural reduction of chemical reaction networks based on topology
• Shortcuts in stochastic systems and control of biophysical processes