Developing an implicit solvent model via a bottom-up approach that can be used in MD simulations represents an important step in extending both time- and length-scales of simulations to study aqueous aggregation and self-assembly.  Even though the primary goal of an implicit solvent model is to properly reproduce a system's thermodynamics, a secondary concern, and one not typically addressed by these models, is to accurately reproduce the dynamics of a solvated system.  We present our Implicit Solvent using the SuperPosition Approximation (IS-SPA) model and how it can be used to not only predict the thermodynamic properties of solvation but also the associated dynamics.  This goal is accomplished by not only studying the mean distribution of water around a solute but also the fluctuations thereof.  The stochastic sampling of the solvent degrees of freedom in IS-SPA can then be parameterized as a solvent bath modeled using Langevin dynamics.  We demonstrate the utility of this model by studying the collapse of non-polar polymers and comparing our model to explicit solvent all-atom molecular dynamics.

Capturing dynamics, along with thermodynamics, in the Implicit Solvent using the SuperPosition Approximation (IS-SPA) model

253rd National Meeting (2017)

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