Stochastic approximation to MBAR and TRAM: batch-wise free energy estimation

TL;DR

This paper introduces stochastic approximation methods for MBAR and TRAM, called SAMBAR and SATRAM, which converge faster in estimating free energies from molecular simulations without losing accuracy.

Contribution

The paper presents novel stochastic algorithms for MBAR and TRAM that improve convergence speed in free energy estimation from molecular simulation data.

Findings

SAMBAR and SATRAM converge faster than traditional methods.

The stochastic methods maintain comparable accuracy to deterministic estimators.

Demonstrated effectiveness on various molecular systems.

Abstract

The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols have been introduced that involve using simulations with biases or changed temperatures. Two established statistically optimal estimators for obtaining unbiased equilibrium properties from such simulations are the multistate Bennett Acceptance Ratio (MBAR) and the transition-based reweighting analysis method (TRAM). Both MBAR and TRAM are solved iteratively and can suffer from long convergence times. Here we introduce stochastic approximators (SA) for both estimators, resulting in SAMBAR and SATRAM, which are shown to converge faster than their deterministic counterparts, without significant accuracy loss. Both methods are demonstrated on different molecular systems.