Transport Coefficients from Large Deviation Functions

TL;DR

This paper introduces a general, efficient method to compute transport coefficients using large deviation functions derived from equilibrium fluctuations, improving statistical accuracy over traditional methods.

Contribution

The paper presents a novel approach employing large deviation functions and diffusion Monte Carlo to calculate transport coefficients more efficiently and accurately.

Findings

Significant statistical improvement over Green-Kubo methods

Method applicable to shear viscosity, interfacial friction, thermal conductivity

Systematic and statistical errors analyzed

Abstract

We describe a method for computing transport coefficients from the direct evaluation of large deviation function. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which is a scaled cumulant generating function analogous to the free energy. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green-Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.