(220f) Replicating the Static and Dynamic Behavior of a Hybrid Fluid Via Relative Resolution

Recently, we presented a novel framework for multiscale simulations, termed "Relative Resolution" (RelRes) [1]. Importantly, our algorithm contains both Fine-Grained (FG) and Coarse-Grained (CG) models in a single fluid system. The 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. Instead of performing an iterative procedure for relating the FG and CG models [2], we formulate for this task an analytical expression which is based on a multipole approximation at appropriate distances: Stemming in a Taylor expansion that conserves energy between a molecular pair in an infinite limit, our algebraic equation allows for parametrization between FG and CG models on a paper sheet instead of a computer cluster. Except that our formalism connects with classical theories in statistical mechanics [3], we also show that RelRes is the natural version of the cell-multipole algorithm for molecular systems [4]: Specifically, our approach groups sites in discrete molecules rather than in continuous cells. With this hybrid formalism for fluid mixtures, we consequently study several systems in molecular simulations (e.g., a liquid-liquid of oxygens-nitrogens, a liquid-gas of tetrachloromethanes-thiophenes, etc.), examining for them both their static and dynamic behavior. We also look at the effect of changing the ratio between the number of FG and CG sites in a given oligomer. As a next step, we proceed by implementing RelRes for water. In summary, RelRes can enhance the computational efficiency of molecular simulations by orders of magnitude, while still capturing the correct phase diagram of various mixtures.

[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. G. Saunders and G. A. Voth. Coarse-graining methods for computational biology. Annual Review of Biophysics 42:73-93, 2013.
[3] B. Widom. Intermolecular forces and the nature of the liquid state. Science 157:375-382, 1967.
[4] L. Greencard and V. Rokhlin. A fast algorithm for particle simulations. Journal of Computational Physics 73:315-348, 1987.