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Combining rules
Combining rules
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In computational chemistry and molecular dynamics, the combination rules or combining rules are equations that provide the interaction energy between two dissimilar non-bonded atoms, usually for the part of the potential representing the van der Waals interaction.[1] In the simulation of mixtures, the choice of combining rules can sometimes affect the outcome of the simulation.[2]

Combining rules for the Lennard-Jones potential

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The Lennard-Jones Potential is a mathematically simple model for the interaction between a pair of atoms or molecules.[3][4] One of the most common forms is

where ε is the depth of the potential well, σ is the finite distance at which the inter-particle potential is zero, r is the distance between the particles. The potential reaches a minimum, of depth ε, when r = 21/6σ ≈ 1.122σ.

Lorentz-Berthelot rules

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The Lorentz rule was proposed by H. A. Lorentz in 1881:[5]

The Lorentz rule is only analytically correct for hard sphere systems. Intuitively, since loosely reflect the radii of particle i and j respectively, their averages can be said to be the effective radii between the two particles at which point repulsive interactions become severe.

The Berthelot rule (Daniel Berthelot, 1898) is given by:[6]

.

Physically, this arises from the fact that is related to the induced dipole interactions between two particles. Given two particles with instantaneous dipole respectively, their interactions correspond to the products of . An arithmetic average of and will not however, result in the average of the two dipole products, but the average of their logarithms would be.

These rules are the most widely used and are the default in many molecular simulation packages, but are not without failings.[7][8][9]

Waldman-Hagler rules

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The Waldman-Hagler rules are given by:[10]

and

Fender-Halsey

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The Fender-Halsey combining rule is given by [11]

Kong rules

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The Kong rules for the Lennard-Jones potential are given by:[12]

Others

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Many others have been proposed, including those of Tang and Toennies[13] Pena,[14][15] Hudson and McCoubrey[16] and Sikora (1970).[17]

Combining rules for other potentials

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Good-Hope rule

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The Good-Hope rule for MieLennard‐Jones or Buckingham potentials is given by:[18]

Hogervorst rules

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The Hogervorst rules for the Exp-6 potential are:[19]

and

Kong-Chakrabarty rules

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The Kong-Chakrabarty rules for the Exp-6 potential are:[20]

and

Other rules for that have been proposed for the Exp-6 potential are the Mason-Rice rules[21] and the Srivastava and Srivastava rules (1956).[22]

Equations of state

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Industrial equations of state have similar mixing and combining rules. These include the van der Waals one-fluid mixing rules

and the van der Waals combining rule, which introduces a binary interaction parameter ,

.

There is also the Huron-Vidal mixing rule, and the more complex Wong-Sandler mixing rule, which equates excess Helmholtz free energy at infinite pressure between an equation of state and an activity coefficient model (and thus with liquid excess Gibbs free energy).

References

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from Grokipedia
Combining rules are mathematical formulations used in and to derive the parameters of intermolecular potentials—such as the collision diameter (σ) and well depth (ε) in the —for interactions between dissimilar atoms or molecules from the corresponding parameters for like interactions. These rules enable the efficient parameterization of force fields by minimizing the need for extensive quantum mechanical calculations on every possible atomic pair, facilitating simulations of complex systems like biomolecules, liquids, and materials. The most common combining rules include the Lorentz–Berthelot rules, which compute σij as the arithmetic mean ((σii + σjj)/2) and εij as the geometric mean (√(εii εjj)) for unlike pairs i and j. The geometric mean rule applies geometric averaging to both parameters (σij = √(σii σjj) and εij = √(εii εjj)), while the Waldman–Hagler rules use a sixth-power mean for σij and a modified expression for εij to better account for polarizability differences (σij = [(σii6 + σjj6)/2]1/6 and εij = √(εii εjj) σii3 σjj3 / σij6) . These approaches are integral to popular force fields such as OPLS, AMBER, CHARMM, and GROMOS, where they model van der Waals forces in non-bonded interactions. The selection of a combining rule influences the fidelity of simulated properties, including densities, vapor pressures, and free energies, with studies showing variations up to several percent across rules for organic liquids. In applications ranging from to , Lorentz–Berthelot rules are often the default due to their simplicity and historical validation against experimental data for and simple fluids, though alternatives like may perform better for polar or hydrogen-bonding systems. Ongoing research optimizes these rules through and benchmarks to enhance predictive accuracy in diverse chemical environments.

Fundamentals

Definition and Purpose

Combining rules are mathematical approximations used in molecular simulations to estimate the interaction parameters for unlike pairs of atoms or molecules from the known parameters of like pairs. In multi-component systems, these rules derive cross-interaction parameters, such as the well depth ε_{ij} and the collision diameter σ_{ij}, without requiring direct experimental determination for every possible pair. This approach is essential for pair potentials that model intermolecular forces, including the , which approximates van der Waals interactions between neutral particles through a balance of repulsive and attractive terms. The primary purpose of combining rules is to reduce the number of parameters that must be specified or fitted in force fields, thereby lowering and enabling efficient simulations of complex mixtures. For a with N distinct atom types, explicitly defining all unlike-pair interactions would necessitate up to N(N-1)/2 additional parameters, which becomes prohibitive for large N; combining rules mitigate this by relying solely on the N like-pair parameters, promoting in and methods. This simplification facilitates the development of transferable force fields applicable to diverse chemical environments while maintaining reasonable accuracy for predicted thermodynamic and transport properties. Combining rules are particularly valuable in modeling binary mixtures of simple fluids, such as noble gases like argon-krypton or argon-xenon, where cross-interactions significantly influence phase equilibria, diffusion coefficients, and . In these cases, the rules allow simulations to capture behavior using parameters optimized for pure components, avoiding the need for extensive experimental data on mixed systems.

Historical Development

The development of combining rules began in the late amid efforts to extend kinetic theory to binary gas mixtures, where simplifying assumptions were needed for unlike-pair interactions in hard-sphere models. In 1881, Hendrik Antoon Lorentz introduced the for molecular diameters, σ_ij = (σ_ii + σ_jj)/2, to approximate collision properties in mixtures without detailed quantum mechanical insights, motivated by the need to fit transport data like viscosities. This additive rule laid the groundwork for handling size differences in classical models. Seventeen years later, in 1898, Daniel Berthelot extended this framework by proposing the geometric mean for attractive energies, ε_ij = √(ε_ii ε_jj), drawing from empirical observations of solubilities and properties to capture dispersion forces in van der Waals equations. Together, these Lorentz-Berthelot rules became the foundational standard for unlike-pair parameters in potentials like Lennard-Jones, prioritizing computational simplicity over exact quantum derivations. By the mid-20th century, limitations in predicting mixture properties such as second virial coefficients prompted refinements based on scattering experiments. In 1962, B. E. F. Fender and G. D. Halsey derived harmonic-mean variants for energy parameters to better align with data, emphasizing improved accuracy for low-temperature behaviors like . This shift reflected growing experimental precision, moving motivations from basic averaging to targeted fitting of virial coefficients and viscosities. In the late 20th century, further adjustments addressed discrepancies in thermodynamic predictions. Chang Lyoul Kong's 1973 rules incorporated hard-sphere corrections to the , motivated by vapor pressure deviations in simulations of simple fluids, yielding closer matches to experimental phase equilibria without additional parameters. Later, in 1993, Marvin Waldman and Arnold T. Hagler proposed modifications accounting for in rare-gas systems, driven by the need for better nonbonded force fields in biomolecular modeling, where size and shape asymmetries affected binding energies. These advancements highlighted a transition toward rules supporting efficiency while enhancing fidelity to diverse datasets. Entering the , critiques underscored ongoing challenges, with Jérôme Delhommelle and Philippe Millié's analysis demonstrating that standard Lorentz–Berthelot rules often failed to predict equilibrium properties accurately in simulations of rare gas mixtures, leading to minor refinements rather than entirely new paradigms. Motivations evolved from early empirical fitting of transport coefficients to broader demands for computational scalability in complex systems, though no major novel rules emerged post-2000, indicating relative maturity in the field.

Combining Rules for the Lennard-Jones Potential

Lorentz–Berthelot Rules

The Lorentz–Berthelot rules represent the most widely adopted combining rules for estimating the unlike-pair parameters in the , particularly for mixtures of non-polar molecules such as . These rules combine the finite distance at which the interparticle potential reaches zero, denoted as σij\sigma_{ij}, via an , and the depth of the , ϵij\epsilon_{ij}, via a . This approach assumes additivity in the repulsive core sizes while treating attractive dispersion forces as multiplicative, providing a straightforward method for modeling intermolecular interactions in molecular simulations without requiring experimental mixture data. The specific formulations are given by the Lorentz rule for the size parameter: σij=σii+σjj2\sigma_{ij} = \frac{\sigma_{ii} + \sigma_{jj}}{2} and the Berthelot rule for the energy parameter: ϵij=ϵiiϵjj\epsilon_{ij} = \sqrt{\epsilon_{ii} \epsilon_{jj}}
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