Exploring the Potential of Parallel Biasing in Flat Histogram Methods

TL;DR

This paper investigates how parallel biasing in flat histogram methods, specifically in metadynamics, improves the exploration and convergence of free energy landscapes across multiple collective variables in molecular simulations.

Contribution

It systematically evaluates the impact of parallel biasing on convergence and addresses bottlenecks in advanced sampling methods for free energy calculations.

Findings

Parallel biasing enhances convergence of free energy landscapes.

It effectively addresses sampling bottlenecks in high-dimensional CV spaces.

The strategy improves exploration efficiency compared to standard methods.

Abstract

Metadynamics, a member of the `flat histogram' class of advanced sampling algorithms, has been widely used in molecular simulations to drive the exploration of states separated by high free energy barriers and promote comprehensive sampling of free energy landscapes defined on collective variables (CVs) which characterize the state of the system. Typically, the methods encounter severe limitations when exploring large numbers of CVs. A recently proposed variant, parallel bias metadynamics (PBMetaD), promises to aid in exploring free energy landscapes along with multiple important collective variables by exchanging the $n$-dimensional free energy landscape required by standard methods for $n$ one-dimensional marginal free energy landscapes. In this study, we systematically examine how parallel biasing affects the convergence of free energy landscapes along with each variable relative to standard methods and the effectiveness of the parallel biasing strategy for addressing common bottlenecks in the use of advanced sampling to calculate free energies.