Calculating particle pair potentials from fluid‐state pair correlations: Iterative ornstein–zernike inversion. Issue 20 (29th April 2018)

• Record Type: Journal Article
• Authors: Heinen, Marco
• Is Part Of: Journal of computational chemistry. Volume 39:Issue 20(2018)
• Page Start: 1531
• Page End: 1543
• Publication Date: 2018-04-29
• Subjects: Monte Carlo simulation -- Ornstein–Zernike equation -- effective interactions -- coarse graining -- Iterative Boltzmann Inversion; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.25225
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : An iterative Monte Carlo inversion method for the calculation of particle pair potentials from given particle pair correlations is proposed in this article. The new method, which is best referred to as Iterative Ornstein–Zernike Inversion, represents a generalization and an improvement of the established Iterative Boltzmann Inversion technique (Reith, Pütz and Müller‐Plathe, J. Comput. Chem. 2003, 24, 1624). Our modification of Iterative Boltzmann Inversion consists of replacing the potential of mean force as an approximant for the pair potential with another, generally more accurate approximant that is based on a trial bridge function in the Ornstein–Zernike integral equation formalism. As an input, the new method requires the particle pair correlations both in real space and in the Fourier conjugate wavenumber space. An accelerated iteration method is included in the discussion, by which the required number of iterations can be greatly reduced below that of the simple Picard iteration that underlies most common implementations of Iterative Boltzmann Inversion. Comprehensive tests with various pair potentials show that the new method generally surpasses the Iterative Boltzmann Inversion method in terms of reliability of the numerical solution for the particle pair potential. © 2018 Wiley Periodicals, Inc. Abstract : The Ornstein–Zernike integral equation formalism is used to improve an established iterative Monte Carlo inversion method. Using fluid‐phase particle pair correlations as its input, the method proposed in this work returns an approximation of the particle pair potential that generally surpasses the result of the related, established Iterative Boltzmann Inversion (IBI) method in its precision. The figure features a comparison of the new Iterative Hypernetted Chain Inversion (IHNCI) method and the IBI method.