SOFTNANACOMP-PHDec 16, 2016

Parameterization of Coarse-grained Molecular Interactions through Potential of Mean Force Calculations and Cluster Expansions Techniques

arXiv:1612.0542920 citationsh-index: 36
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This work offers a systematic method for coarse-graining molecular interactions, but the improvements are incremental for liquid regimes, limiting its immediate impact.

The authors present a coarse-graining strategy using cluster expansion techniques to construct interaction potentials for molecular systems. They find that the cluster expansion provides accurate effective pair and three-body potentials at high temperature and low density, but in the liquid regime, three-body terms offer only small improvements over pair potentials, requiring higher-order terms for significant gains.

We present a systematic coarse-graining (CG) strategy for many particle molecular systems based on cluster expansion techniques. We construct a hierarchy of coarse-grained Hamiltonians with interaction potentials consisting of two, three and higher body interactions. The accuracy of the derived cluster expansion based on interatomic potentials is examined over a range of various temperatures and densities and compared to direct computation of pair potential of mean force. The comparison of the coarse-grained simulations is done on the basis of the structural properties, against the detailed all-atom data. We give specific examples for methane and ethane molecules in which the coarse-grained variable is the center of mass of the molecule. We investigate different temperature and density regimes, and we examine differences between the methane and ethane systems. Results show that the cluster expansion formalism can be used in order to provide accurate effective pair and three-body CG potentials at high $T$ and low $ρ$ regimes. In the liquid regime the three-body effective CG potentials give a small improvement, over the typical pair CG ones; however in order to get significantly better results one needs to consider even higher order terms.

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