Equilibrium vapor pressure and surface tension from cluster data: density functional results

Area of Science:

• Computational Chemistry
• Physical Chemistry
• Materials Science

Background:

• Predicting bulk thermodynamic properties from molecular simulations is crucial.
• Traditional methods often require large system sizes or complex simulations.
• Investigating smaller systems like clusters offers a potential shortcut.

Purpose of the Study:

• To assess the feasibility of using atomic and molecular cluster data for predicting bulk equilibrium vapor pressure and surface tension.
• To validate a method for extracting bulk properties from constrained cluster simulations.
• To compare these predictions with exact values derived from density functional theory.

Main Methods:

• Density functional theory (DFT) calculations were employed.
• Free energies of center-of-mass clusters, confined to a spherical volume, were computed at various temperatures.
• Lennard-Jones atoms and two-site Lennard-Jones molecules were used as model systems.
• The Merikanto et al. method was applied to extract bulk properties from cluster data.

Main Results:

• Estimates for equilibrium vapor pressure and surface tension were within 4% of exact DFT values at temperatures near the triple point.
• Accuracy was maintained for molecular models with structured liquid-vapor interfaces.
• The predictive capability was reliable for clusters containing over approximately 100 molecules.
• The influence of the constraining volume on the results was found to be minimal.

Conclusions:

• Atomic and molecular cluster data, when analyzed appropriately, can reliably predict equilibrium vapor pressure and surface tension.
• This approach offers an efficient alternative for determining key thermodynamic properties.
• The findings support the use of cluster-based methods in computational thermodynamics and materials science.