Dynamical properties across different coarse-grained models for ionic liquids
arXiv:2102.02761 · doi:10.1088/1361-648X/abe6e1
Abstract
Room-temperature ionic liquids (RTILs) stand out among molecular liquids for their rich physicochemical characteristics, including structural and dynamic heterogeneity. The significance of electrostatic interactions in RTILs results in long characteristic length- and timescales, and has motivated the development of a number of coarse-grained (CG) simulation models. In this study, we aim to better understand the connection between certain CG parametrization strategies and the dynamical properties and transferability of the resulting models. We systematically compare five CG models: a model largely parametrized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parametrizations and reference temperatures. All five CG models display limited structural transferability over temperature, and also result in various effective dynamical speedup factors, relative to a reference atomistic model. On the other hand, the structure-based CG models tend to result in more consistent cation-anion relative diffusion than the thermodynamic-based models, for a single thermodynamic state point. By linking short- and long-timescale dynamical behaviors, we demonstrate that the varying dynamical properties of the different coarse-grained models can be largely collapsed onto a single curve, which provides evidence for a route to constructing dynamically-consistent CG models of RTILs.
References in corpus (13)
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials
- Representability problems for coarse-grained water potentials
- Mapping atomistic to coarse-grained polymer models using automatic simplex optimization to fit structural properties
- DeePCG: constructing coarse-grained models via deep neural networks
- Unified Theory of Activated Relaxation in Liquids over 14 Decades in Time
- Resolving Dynamic Properties of Polymers through Coarse-Grained Computational Studies
- Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
- Understanding three-body contributions to coarse-grained force-fields
- Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information
- Accurate structure-based coarse-graining leads to consistent barrier-crossing dynamics
- Concurrent parametrization against static and kinetic information leads to more robust coarse-grained force fields
- Dynamical properties across different coarse-grained models for ionic liquids
- Coarse-grained conformational surface hopping: Methodology and transferability