Estimating thermodynamic expectations and free energies in expanded ensemble simulations: systematic variance reduction through conditioning

TL;DR

This paper introduces a variance reduction method using conditioning in expanded ensemble simulations to improve the accuracy of thermodynamic expectation and free energy estimates, demonstrated through phase transition analysis.

Contribution

It presents a systematic approach to applying conditioning for variance reduction in expanded ensemble Monte Carlo simulations, enhancing thermodynamic estimations.

Findings

Variance reduction is theoretically proven and practically demonstrated.

Efficient free energy estimation in phase transition scenarios.

Improved accuracy over standard estimators.

Abstract

Markov chain Monte Carlo methods are primarily used for sampling from a given probability distribution and estimating multi-dimensional integrals based on the information contained in the generated samples. Whenever it is possible, more accurate estimates are obtained by combining Monte Carlo integration and integration by numerical quadrature along particular coordinates. We show that this variance reduction technique, referred to as conditioning in probability theory, can be advantageously implemented in \emph{expanded ensemble} simulations. These simulations aim at estimating thermodynamic expectations as a function of an external parameter that is sampled like an additional coordinate. Conditioning therein entails integrating along the external coordinate by numerical quadrature. We prove variance reduction with respect to alternative standard estimators and demonstrate the practical efficiency of the technique by estimating free energies and characterizing a structural phase transition between two solid phases.