Use the force! Reduced variance estimators for densities, radial distribution functions and local mobilities in molecular simulations

TL;DR

This paper reviews recent methods that use particle forces to create reduced variance estimators for local properties in molecular simulations, improving accuracy without significant additional computational cost.

Contribution

It introduces force-based reduced variance estimators for densities and radial distribution functions, addressing limitations of traditional histogram methods in molecular simulations.

Findings

Force-based estimators reduce variance compared to histograms.

The approach is computationally efficient in molecular dynamics.

Strategies mitigate artifacts in force sampling methods.

Abstract

Even though the computation of local properties, such as densities or radial distribution functions, remains one of the most standard goals of molecular simulation, it still largely relies on straighforward histogram-based strategies. Here we highlight recent developments of alternative approaches leading, from different perspectives, to estimators with a reduced variance compared to conventional binning. They all make use of the force acting on the particles, in addition to their position, and allow to focus on the non-trivial part of the problem in order to alleviate (or even remove in some cases) the catastrophic behaviour of histograms as the bin size decreases. The corresponding computational cost is negligible for molecular dynamics simulations, since the forces are already computed to generate the configurations, and the benefit of reduced-variance estimators is even larger when the cost of generating the latter is high, in particular with ab initio simulations. The force sampling approach may result in spurious residual non-zero values of the density in regions where no particles are present, but strategies are available to mitigate this artefact. We illustrate this approach on number, charge and polarization densities, radial distribution functions and local transport coefficients, discuss the connections between the various perspectives and suggest future challenges for this promising approach.