Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach

Lyubartsev, Alexander P.; Laaksonen, Aatto

doi:10.1103/physreve.52.3730

Cited by 787 publications

(833 citation statements)

References 18 publications

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“…Here, we focus on the inverse Monte Carlo (IMC) method introduced by Lyubartsev and Laaksonen in 1995. 77 We would also like to point out that there are new simplex-algorithm based optimization procedures developed by Mu¨ller-Plathe et al [78][79][80] that exploit the above described relation. Next, we will focus on the IMC method, and in section IID we present two examples of how to use IMC to construct potentials for lipid membrane systems.…”

Section: B Methods To Solve the Inverse Problemmentioning

confidence: 99%

Multiscale modeling of emergent materials: biological and soft matter

Murtola

Bunker

Vattulainen

et al. 2009

Phys. Chem. Chem. Phys.

260

234

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On using a too large integration time step in molecular dynamics simulations of coarse-grained molecular models Moritz Winger, Daniel Trzesniak, Riccardo Baron and Wilfred F. In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.

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Section: B Methods To Solve the Inverse Problemmentioning

confidence: 99%

Multiscale modeling of emergent materials: biological and soft matter

Murtola

Bunker

Vattulainen

et al. 2009

Phys. Chem. Chem. Phys.

260

234

View full text Add to dashboard Cite

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“…If the system is also isotropic, the two-particle correlation function is equal to its translational and orientational average: (27) Equation (27) defines an operator which averages over both translation and rotation of the system, where Ω j,i defines the orientation of the vector r⃑ j,i from the i to the j CG site. For an isotropic homogeneous system the gradient term in eqs (26) may then be simplified as Under the additional assumption that the pair interaction between CG sites is central such that, (28) eq (26) may be re-expressed to read (29) Projecting this equation onto the vector u⃑ i,j and shifting the integration variable, one obtains the following result: (30) Thus the dot product factor arises naturally in an integral equation theory, just as it did in the MS-CG equations. For a system described by a central pair potential without an external field, the average effect of a third particle on two-particle correlations must lie along the two-particle vector.…”

Section: Yvon-born-green Equationmentioning

confidence: 99%

Multiscale Coarse-Graining and Structural Correlations: Connections to Liquid-State Theory

et al. 2007

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(2005)]. If no approximations are made, the MS-CG method yields a many-body multi-dimensional potential of mean force describing the interactions between CG sites. However, numerical applications of the MS-CG method typically employ a set of pair potentials to describe non-bonded interactions. The analogy between coarse-graining and the inverse problem of liquid state theory clarifies the general significance of three-particle correlations for the development of such CG pair potentials. It is demonstrated that the MS-CG methodology incorporates critical three-body correlation effects and that, for isotropic homogeneous systems evolving under a central pair potential, the MS-CG equations are a discretized representation of the well-known Yvon-Born-Green equation. Numerical calculations validate the theory and illustrate the role of these structural correlations in the MS-CG method.

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“…Differences in g(r) directly translate into differences in V PMF (r). If higher order corrections are included, a different kind of potential is found, termed effective potential V eff (r) [12]. The effective potential is defined by the condition that in the canonical ensemble it yields the desired radial-distribution function.…”

Section: H Implications For the Effective Potentialmentioning

confidence: 99%

“…The water models used for the aqueous solution were SPC, SPC/E, TIP3P and TIP4P. In previous studies some static properties such as radial distribution functions have been studied, but in each case for one particular choice of force field only [7,8,9,10,11,12,16]. In addition, the effects of temperature [17] and salt concentration [18] have recently been studied.…”

Section: Introductionmentioning

confidence: 99%

Systematic comparison of force fields for microscopic simulations of NaCl in aqueous solutions: Diffusion, free energy of hydration, and structural properties

Patra¹,

Karttunen²

2004

J Comput Chem

209

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In this paper we compare different force fields that are widely used (Gromacs, Charmm-22/xPlor, Charmm-27, Amber-1999, OPLS-AA) in biophysical simulations containing aqueous NaCl. We show that the uncertainties of the microscopic parameters of, in particular, sodium and, to a lesser extent, chloride translate into large differences in the computed radial-distribution functions. This uncertainty reflects the incomplete experimental knowledge of the structural properties of ionic aqueous solutions at finite molarity. We discuss possible implications on the computation of potential of mean force and effective potentials.

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Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach

Cited by 787 publications

References 18 publications

Multiscale modeling of emergent materials: biological and soft matter

Multiscale modeling of emergent materials: biological and soft matter

Multiscale Coarse-Graining and Structural Correlations: Connections to Liquid-State Theory

Systematic comparison of force fields for microscopic simulations of NaCl in aqueous solutions: Diffusion, free energy of hydration, and structural properties

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