Relative resolution: A hybrid formalism for fluid mixtures

Chaimovich, Aviel; Peter, Christine; Kremer, Kurt

doi:10.1063/1.4929834

Cited by 10 publications

(80 citation statements)

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“…Conversely, we developed an analytical parameterization between the FG and CG models, which is just based on energy conservation [1]. Unlike those other strategies [38,39], we were consequently able of correctly retrieving across state space, the structural and thermal behavior of several nonpolar mixtures [1]. Besides, we showed that our hybrid approach can be considered as a generalized extension of established theories for uniform liquids, which assume a mean field for interactions beyond a certain distance [40,41,42,43].…”

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confidence: 98%

“…Once the two models are analytically connected with each other via energy conservation, Relative Resolution can capture the structural and thermal behavior of (nonpolar) multi-component and multi-phase systems across state space. The current work is a natural continuation of our original communication [1]. Most importantly, we present the comprehensive mathematics of Relative Resolution, basically casting it as a multipole approximation at appropriate distances; the current set of equations importantly applies for all systems (e.g, polar molecules).…”

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confidence: 99%

“…Very recently, we developed another type of multiscale methods, termed Relative Resolution (RelRes) [1]. Inspired by Adaptive Resolution, this approach is a "two-body distance-based" multiscale method: In a single molecular simulation, hybrid molecules switch their resolution in terms of relative separation; in particular, molecules that are near neighbors (i.e., their pairwise distance is small) interact via their FG models, and molecules that are far neighbors (i.e., their pairwise distance is large) interact via their CG models [1]. Importantly, RelRes is the sole class of multiscale simulations which describes all molecules, for all times, with both FG and CG models; the resolution of a given molecule is always relative of its observer.…”

mentioning

confidence: 99%

“…[39] does not make a clear connection between the two models. Conversely, we developed an analytical parameterization between the FG and CG models, which is just based on energy conservation [1]. Unlike those other strategies [38,39], we were consequently able of correctly retrieving across state space, the structural and thermal behavior of several nonpolar mixtures [1].…”

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confidence: 99%

“…In this current work, we build on our original communication of RelRes [1]. Foremost, we mathematically present the comprehensive framework for RelRes.…”

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confidence: 99%

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Relative resolution: Multipole approximation at appropriate distances

Chaimovich¹

2020

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Recently, we introduced Relative Resolution as a hybrid formalism for fluid mixtures [1]. The essence of this approach is that it switches molecular resolution in terms or relative separation: While nearest neighbors are characterized by a detailed fine-grained model, other neighbors are characterized by a simplified coarse-grained model. Once the two models are analytically connected with each other via energy conservation, Relative Resolution can capture the structural and thermal behavior of (nonpolar) multi-component and multi-phase systems across state space. The current work is a natural continuation of our original communication [1]. Most importantly, we present the comprehensive mathematics of Relative Resolution, basically casting it as a multipole approximation at appropriate distances; the current set of equations importantly applies for all systems (e.g, polar molecules). Besides, we continue examining the capability of our multiscale approach in molecular simulations, importantly showing that we can successfully retrieve not just the statics but also the dynamics of liquid systems. We finally conclude by discussing how Relative Resolution is the inherent variant of the famous "cell-multipole" approach for molecular simulations.Over the past half of a century, invoking molecular simulations has become one of the most promising routes for studying soft matter. This computational approach is splendid for describing systems that just comprise small spatial and short temporal dimensions (i.e., < 10 −6 m and < 10 −6 s, respectively), but it is deficient in describing systems that also involve large spatial and long temporal dimensions (i.e., > 10 −3 m and > 10 −3 s, respectively); resolving such challenges is especially important for biological processes, whose particularities usually span orders of magnitude in scale. For overcoming this dimensionality issue, special attention has been given for enhancing the computational efficiency of molecular simulations, while ensuring that the phenomena of interest is still correctly retrieved. One route involves the intelligent use of statistical mechanics while designing sophisticated algorithms. For example, various successful strategies have been developed that focus on improving the computational efficiency of specific aspects of molecular simulations (e.g., free energies [2,3,4,5], reaction coefficients [6,7,8,9], etc.).Our work revolves around another set of algorithms that has received much attention in the recent couple of decades: It is commonly called the multiscale approach, and it generally takes on the in-serial or in-parallel format. Rather than focusing on the calculation of a specific feature of a molecular simulation, multiscale algorithms aim at improving the computational efficiency of the entire system, while ideally capturing all of its static and dynamic behavior. Importantly, the main signature of all multiscale simulations is that they involve two systems, one constructed of detailed Fine-Grained (FG) models with many degrees of freedom (usu...

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confidence: 98%

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confidence: 99%

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confidence: 99%

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confidence: 99%

“…In this current work, we build on our original communication of RelRes [1]. Foremost, we mathematically present the comprehensive framework for RelRes.…”

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confidence: 99%

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