Simulating prescribed particle densities in the grand canonical ensemble using iterative algorithms

Attila Malasics, Dirk Gillespie, D. Boda

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

We present two efficient iterative Monte Carlo algorithms in the grand canonical ensemble with which the chemical potentials corresponding to prescribed (targeted) partial densities can be determined. The first algorithm works by always using the targeted densities in the kT log (ρ i) (ideal gas) terms and updating the excess chemical potentials from the previous iteration. The second algorithm extrapolates the chemical potentials in the next iteration from the results of the previous iteration using a first order series expansion of the densities. The coefficients of the series, the derivatives of the densities with respect to the chemical potentials, are obtained from the simulations by fluctuation formulas. The convergence of this procedure is shown for the examples of a homogeneous Lennard-Jones mixture and a NaCl-Ca Cl2 electrolyte mixture in the primitive model. The methods are quite robust under the conditions investigated. The first algorithm is less sensitive to initial conditions.

Original languageEnglish
Article number124102
JournalThe Journal of Chemical Physics
Volume128
Issue number12
DOIs
Publication statusPublished - 2008

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Chemical potential
iteration
ideal gas
series expansion
Electrolytes
Gases
electrolytes
Derivatives
coefficients
simulation

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

Cite this

Simulating prescribed particle densities in the grand canonical ensemble using iterative algorithms. / Malasics, Attila; Gillespie, Dirk; Boda, D.

In: The Journal of Chemical Physics, Vol. 128, No. 12, 124102, 2008.

Research output: Contribution to journalArticle

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