An efficient iterative grand canonical Monte Carlo algorithm to determine individual ionic chemical potentials in...

Area of Science:

• Computational chemistry
• Physical chemistry
• Chemical thermodynamics

Background:

• Grand canonical Monte Carlo (GCMC) simulations are used to determine chemical potentials.
• Existing iterative methods require repeated GCMC simulations to achieve target concentrations.
• Challenges arise with charge neutrality when simulating individual ions versus neutral salts.

Purpose of the Study:

• To improve the efficiency of calculating chemical potentials from GCMC simulations.
• To combine a robust iteration algorithm with a chemical potential averaging technique.
• To address convergence issues and enable accurate calculations for individual ions.

Main Methods:

• Implementing an iterative algorithm combining prior work (Malasics et al., Lamperski).
• Averaging excess chemical potentials across iterations instead of using only the last value.
• Applying the unified method to various electrolyte solutions.

Main Results:

• The proposed averaging procedure significantly enhances the efficiency of the GCMC iterative method.
• The unified algorithm demonstrates improved performance for electrolyte solutions.
• A correction term is introduced to handle convergence issues with individual ion simulations.

Conclusions:

• The enhanced iterative GCMC method with chemical potential averaging is more efficient for determining chemical potentials.
• The algorithm is effective for various electrolyte solutions and can be adapted for individual ion calculations.
• The suggested correction term resolves convergence problems related to charge neutrality violations.