Bottom-Up Informed and Iteratively Optimized Coarse-Grained Non-Markovian Water Models with Accurate Dynamics

Abstract

Molecular dynamics (MD) simulations based on coarse-grained (CG) particle models of molecular liquids generally predict accelerated dynamics and misrepresent the time scales for molecular vibrations and diffusive motions. The parametrization of Generalized Langevin Equation (GLE) thermostats based on the microscopic dynamics of the fine-grained model provides a promising route to address this issue, in conjunction with the conservative interactions of the CG model obtained with standard coarse graining methods, such as iterative Boltzmann inversion, force matching, or relative entropy minimization. We report the application of a recently introduced bottom-up dynamic coarse graining method, based on the Mori–Zwanzig formalism, which provides accurate estimates of isotropic GLE memory kernels for several CG models of liquid water. We demonstrate that, with an additional iterative optimization of the memory kernels (IOMK) for the CG water models based on a practical iterative optimization technique, the velocity autocorrelation function of liquid water can be represented very accurately within a few iterations. By considering the distinct Van Hove function, we demonstrate that, with the presented methods, an accurate representation of structural relaxation can be achieved. We consider several distinct CG potentials to study how the choice of the CG potential affects the performance of bottom-up informed and iteratively optimized models.

Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties

“…One has to be aware that after the above treatment, it is still impossible to obtain both the Rouse term and the non-Markovian term on the right side of eq analytically. Alternatively, one can turn to iterative construction processes. ,− ,, According to ref , we set velocity ACF from AA simulation as the target during the iterative optimization of the kernel as follows k i + 1 ( t ) = k i ( t ) − α h i ( t ) ∂ ∂ t ( ⟨ V ( t ) V ( 0 ) ⟩ A A − ⟨ V ( t ) V ( 0 ) ⟩ G L E ) with h i ( t ) = { lefttrue 1 t / t c o r ≤ i 2 1 − t / t c o r + i / 2 i 2…”

“…Note that during the iteration process, we need to fix the total friction at a certain value, ζ eff * , such that the diffusion coefficient resulted from CG simulation matches with that of AA simulation, i.e., D CG = D AA . There are some similarities between our method with refs and , where they use the integral of memory kernel ζ ( t ) = ∫ 0 t normald s K ( s ) as the optimization target. In addition, the kernel is treated numerically during the iteration process.…”

“…Therefore, incorporating memory kernel terms is necessary to develop dynamically consistent non-Markovian CG models . Such an approach has been successful in developing dynamically consistent CG models for simple molecule liquids, − star polymers, − and colloids, − capturing the dynamical properties from AA model at all frequencies. In these models, each molecule, star polymer, and colloidal nanoparticle is typically CG into one CG bead.…”

Force renormalization for probes immersed in an active bath

“….i, will therefore refer to the average over this, possibly non-equilibrium, ensemble. One should note that one has some freedom as to how to distribute the total force between f, K and C. 17,30,49 At equilibrium, a popular Ansatz 33,48,50,51 is to assume that f subsumes all reversible interactions (which also include medium-mediated interactions) and therefore correspond to the force emerging from static coarse-graining. The other two terms then characterize the thermal coupling with the medium; i.e., they can be seen as a thermostat 52 with colored noise.…”

Mobility, response and transport in non-equilibrium coarse-grained models

“…In addition to effective (pair) potentials known from structural coarse-graining [5] the GLE features memory kernels and fluctuating forces to model the frictional interactions and thermal fluctuations in the system. For equilibrium systems, a manifold of different dynamic coarse-graining techniques has been suggested which can be used to systematically derive such GLEs [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], showing the importance and actuality of the topic. The applicability of these methods to nonequilibrium systems is, however, under strong debate.…”