Estimating equilibrium ensemble averages using multiple time slices from driven nonequilibrium processes: theory and application to free energies, moments, and thermodynamic length in single-molecule pulling experiments

TL;DR

This paper introduces a generalized estimator that combines data from multiple time slices in driven nonequilibrium processes to accurately compute equilibrium averages, improving efficiency and precision in single-molecule experiments.

Contribution

It extends existing methods by pooling information across multiple time slices, enabling more accurate and smoother estimates of equilibrium properties from nonequilibrium data.

Findings

Estimator yields smoother equilibrium property estimates.

Lower variance achieved for higher-order moments.

Effective in calculating free energies and thermodynamic metrics.

Abstract

Recently discovered identities in statistical mechanics have enabled the calculation of equilibrium ensemble averages from realizations of driven nonequilibrium processes, including single-molecule pulling experiments and analogous computer simulations. Challenges in collecting large data sets motivate the pursuit of efficient statistical estimators that maximize use of available information. Along these lines, Hummer and Szabo developed an estimator that combines data from multiple time slices along a driven nonequilibrium process to compute the potential of mean force. Here, we generalize their approach, pooling information from multiple time slices to estimate arbitrary equilibrium expectations. Our expression may be combined with estimators of path-ensemble averages, including existing optimal estimators that use data collected by unidirectional and bidirectional protocols. We demonstrate the estimator by calculating free energies, moments of the polymer extension, and the metric tensor for thermodynamic length in a model single-molecule pulling experiment. Compared to estimators that only use individual time slices, our multiple time-slice estimators yield substantially smoother estimates and achieve lower variance for higher-order moments.