Communication: Predicting virial coefficients and alchemical transformations by extrapolating Mayer-sampling Monte...

Area of Science:

• Statistical Mechanics
• Computational Chemistry
• Thermodynamics

Background:

• Virial coefficients are crucial for understanding fluid behavior.
• Traditional methods for calculating virial coefficients can be computationally intensive.
• Predicting these coefficients across wide parameter ranges is challenging.

Purpose of the Study:

• To develop an efficient method for predicting virial coefficients.
• To extend the applicability of Mayer-sampling Monte Carlo simulations.
• To accurately determine virial coefficients over broad temperature and model parameter ranges.

Main Methods:

• Utilized Mayer-sampling Monte Carlo simulations.
• Employed statistical mechanical extrapolation techniques.
• Applied the method to the SPC/E water model and Lennard-Jones fluid.

Main Results:

• Accurately predicted the second virial coefficient for SPC/E water over four orders of magnitude.
• Achieved less than 2% deviation in predictions.
• Successfully predicted coefficients for scaled site charges and the Lennard-Jones fluid's exponential parameter.

Conclusions:

• Statistical mechanical extrapolation offers a computationally efficient approach for virial coefficient prediction.
• The method provides accurate, continuous predictions across wide ranges.
• This technique enhances the utility of simulations for thermodynamic property calculations.