(70h) Employing a Multipole Approximation in a Hybrid Fluid Via Relative Resolution

Recently, we presented a novel framework for multiscale simulations, termed "Relative Resolution" (RelRes) [1]. Inspired by another hybrid approach [2], our algorithm also contains both Fine-Grained (FG) and Coarse-Grained (CG) models in asingle fluid system. However, a unique feature of RelRes is that it switches between molecular resolution in terms of relative separation: While nearest neighbors are characterized by a FG (geometrically detailed) model, other neighbors are characterized by a CG (isotropically simplified) model. We in turn make important connections with classical theories in statistical mechanics [3]. Instead of performing an iterative procedure for relating the FG and CG models [4], we formulate for this task an analytical expression which is based on a multipole approximation at appropriate distances: This algebraic equation stems in the energy conservation between a molecular pair in an infinite limit, and it substantially enhances the computational efficiency of RelRes. With this hybrid formalism for fluid mixtures, we consequently examine several systems in molecular simulations (e.g., a liquid-liquid of oxygens-nitrogens, a liquid-gas of tetrachloromethanes-thiophene, etc.). We primarily focus on structural correlations and on thermal properties, correctly retrieving their corresponding behavior across state space. As a next step, we also proceed by implementing RelRes for polymers. In summary, we show here for fluids, that molecular resolution is inherently hybrid in terms of relative separation.

[1] A. Chaimovich, C. Peter, and K. Kremer. Relative resolution: A hybrid formalism for fluid mixtures. The Journal of Chemical Physics 143:243107, 2015.

[2] M. Praprotnik, L. Delle Site, and K. Kremer. Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly. The Journal of Chemical Physics 123:224106, 2005. 
[3] B. Widom. Intermolecular forces and the nature of the liquid state. Science 157:375-382, 1967.
[4] M. G. Saunders and G. A. Voth. Coarse-graining methods for computational biology. Annual Review of Biophysics 42:73-93, 2013.