Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties

Abstract

Coarse-grained (CG) models can provide computationally efficient and conceptually simple characterizations of soft matter systems. While generic models probe the underlying physics governing an entire family of free-energy landscapes, bottom-up CG models are systematically constructed from a higher-resolution model to retain a high level of chemical specificity. The removal of degrees of freedom from the system modifies the relationship between the relative time scales of distinct dynamical processes through both a loss of friction and a "smoothing" of the free-energy landscape. While these effects typically result in faster dynamics, decreasing the computational expense of the model, they also obscure the connection to the true dynamics of the system. The lack of consistent dynamics is a serious limitation for CG models, which not only prevents quantitatively accurate predictions of dynamical observables but can also lead to qualitatively incorrect descriptions of the characteristic dynamical processes. With many methods available for optimizing the structural and thermodynamic properties of chemically-specific CG models, recent years have seen a stark increase in investigations addressing the accurate description of dynamical properties generated from CG simulations. In this review, we present an overview of these efforts, ranging from bottom-up parametrizations of generalized Langevin equations to refinements of the CG force field based on a Markov state modeling framework. We aim to make connections between seemingly disparate approaches, while laying out some of the major challenges as well as potential directions for future efforts.

Coarse-grained model of a nanoscale-segregated ionic liquid for simulations of low-temperature structure and dynamics

“…This effect is further illustrated in the inset, which reveals that the speedup factor D CG /D AA increases from ∼10 at T = 400 K to values of more than 1000 near T = 250 K. Similar discrepancies between AA and CG models of glass-forming ILs were reported in previous simulation studies [31,41]. In the literature, various strategies, e.g., generalized Langevin equation approaches, were proposed to cope with such speedup of dynamics when removing degrees of freedom and smoothing the energy landscapes upon coarse graining [54]. Since the structural properties of ILs are the main focus of the present study, we do not pursue this promising but still challenging route.…”

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

“…From Equation ( 1 ), it follows that the net motion of the CG degrees of freedom is governed by the mean forces (the forces averaged over the atoms that constitute the interaction sites), while the fine-grained degrees of freedom contribute to the motion of the CG degrees of freedom through the friction forces (which depend on the time correlation of the fluctuations of the forces acting on the sites) and the fluctuating forces. The friction forces depend on the whole history of the correlation between the fluctuating forces and the velocities of the coarse-grained centers [ 17 , 34 ]. A number of studies were carried out with liquids or simple polymers to determine the friction term exactly based on all-atom simulations.…”

“…In practical implementations, the friction and the stochastic force terms are taken from the Langevin equation [ 35 ], which is equivalent to the assumption that the fluctuating forces and the coarse-grained velocities are correlated only over an infinitesimally small period of time ( -correlated); in other words, it is assumed that the fine-grained degrees of freedom move much faster than the coarse-grained ones. This results in replacing the last two terms on the right-hand side of Equation ( 1 ) with the net friction and stochastic force terms, respectively (Equation ( 4 )) [ 15 , 17 , 21 , 34 , 36 , 37 ]. where is the inertia matrix, , and are the generalized coarse-grained coordinates, velocities and accelerations, respectively, U is the effective coarse-grained energy function (which originates in the potential of mean force; see Section 2.3 ) [ 24 , 25 , 27 ], is the friction matrix, and are the random forces.…”

Interplay between surfactant self-assembly and adsorption at hydrophobic surfaces: insights from dissipative particle dynamics

“…where e ij = r ij /r ij is the unit vector. Generally, coarse-graining (removing degrees of freedom from the system) causes a loss of friction and "smoothing" of the free-energy landscape, leading to faster dynamics at the mesoscale level [42]. In DPD, the removed degrees of freedom can be effectively returned to the system using pair-wise dissipative and random forces…”