Improved grand canonical sampling of vapour-liquid transitions

TL;DR

This paper introduces two novel biasing methods in grand canonical simulations that improve sampling efficiency of vapour-liquid transitions, especially at low temperatures and large system sizes, by better handling droplet shape changes.

Contribution

The paper presents a droplet shape-based order parameter and a subvolume biasing technique to enhance sampling of phase transitions in grand canonical simulations.

Findings

Improved sampling efficiency over standard methods.

Accurate estimates of coexistence parameters and surface tension.

Effective at larger system sizes and lower temperatures.

Abstract

Simulation within the grand canonical ensemble is the method of choice for accurate studies of first order vapour-liquid phase transitions in model fluids. Such simulations typically employ sampling that is biased with respect to the overall number density in order to overcome the free energy barrier associated with mixed phase states. However, at low temperature and for large system size, this approach suffers a drastic slowing down in sampling efficiency. The culprits are geometrically induced transitions (stemming from the periodic boundary conditions) which involve changes in droplet shape from sphere to cylinder and cylinder to slab. Since the overall number density doesn't discriminate sufficiently between these shapes, it fails as an order parameter for biasing through the transitions. Here we report two approaches to ameliorating these difficulties. The first introduces a droplet shape based order parameter that generates a transition path from vapour to slab states for which spherical and cylindrical droplet are suppressed. The second simply biases with respect to the number density in a tetragonal subvolume of the system. Compared to the standard approach, both methods offer improved sampling, allowing estimates of coexistence parameters and vapor-liquid surface tension for larger system sizes and lower temperatures.