Coarse-grained models include only the most important degrees of freedom to match certain target properties and thus reduce the computational costs. The dynamics of these models is usually accelerated compared to those of the parent atomistic models. We propose a new approach to predict this acceleration on the basis of the loss of geometric information upon coarse-graining. To this end, the molecular roughness difference is calculated by a numerical comparison of the molecular surfaces of both the atomistic and the coarse-grained systems. Seven homogeneous hydrocarbon liquids are coarse-grained using the structure-based iterative Boltzmann inversion. An acceleration factor is calculated as the ratio of diffusion coefficients of the coarse-grained and atomistic simulation. The molecular roughness difference and the acceleration factor of the seven test systems reach a very good linear correlation.

Coarse-grained molecular dynamics (MD) simulation is a promising alternative to all-atom MD simulation for the fast calculation of system properties, which is imperative in designing materials with a specific target property. There have been several coarse-graining strategies developed over the past few years that provide accurate structural properties of the system. However, these coarse-grained models share a major drawback in that they introduce an artificial acceleration in molecular mobility. In this paper, we report a data-driven approach to generate coarse-grained models that preserve the all-atom molecular mobility. We designed a machine learning model in the form of an artificial neural network, which directly predicts the simulation-ready mobility-preserving coarse-grained potential as an output given the all-atom force field (FF) parameters as inputs. As a proof of principle, we took 2,3,4-trimethylpentane as a model system and described the development of machine learning models in detail. We quantify the artificial acceleration in molecular mobility by defining the acceleration factor as the ratio of the coarse-grained and the all-atom diffusion coefficient. The predicted coarse-grained potential generated by the best machine learning model can bring down the acceleration factor to a value of ∼2, which could be otherwise as large as 7 for a typical value of 3 × 10–9 m2 s–1 for the all-atom diffusion coefficient. We believe our method will be of interest in the community as a route to generating coarse-grained potentials with accurate dynamics.

Ethanol is highly effective against various enveloped viruses and can disable the virus by disintegrating the protective envelope surrounding it. The interactions between the coronavirus envelope (E) protein and its membrane environment play key roles in the stability and function of the viral envelope. By using molecular dynamics simulation, we explore the underlying mechanism of ethanol-induced disruption of a model coronavirus membrane and, in detail, interactions of the E-protein and lipids. We model the membrane bilayer as N-palmitoyl-sphingomyelin and 1-palmitoyl-2-oleoylphosphatidylcholine lipids and the coronavirus E-protein. The study reveals that ethanol causes an increase in the lateral area of the bilayer along with thinning of the bilayer membrane and orientational disordering of lipid tails. Ethanol resides at the head–tail region of the membrane and enhances bilayer permeability. We found an envelope-protein-mediated increase in the ordering of lipid tails. Our simulations also provide important insights into the orientation of the envelope protein in a model membrane environment. At ∼25 mol. % of ethanol in the surrounding ethanol–water phase, we observe disintegration of the lipid bilayer and dislocation of the E-protein from the membrane environment.

We study the collapse of a linear bead-spring chain under a sudden quench in solvent conditions using explicit-solvent dissipative particle dynamics. We investigate the collapse stages of our 50 ≤ N ≤ 1000 bead chains by studying local structures identified by an extended clustering algorithm. We find evidence for the three early stages proposed by Halperin and Goldbart [Phys. Rev. E 2000, 61, 565–573]. Their apparent scaling with the chain length is ∝N 0, N 0.82(6), and N 1.04(2). These values are similar to the predicted ones, and deviations likely stem from the approximations made. The scaling of the overall collapse time with the chain length τc ∝ N 0.94(2), the decay of the squared radius of gyration (R g 2(0) – R g 2(t)) ∝ t 1.09(1), and the growth of blobs along the chain ⟨Sn ⟩ ∝ t 1 are all found to be approximately linear.

The reduced number of degrees of freedom in a coarse-grained molecular model compared to its parent atomistic model not only makes it possible to simulate larger systems for longer time scales but also results in an artificial mobility increase. The RoughMob method [J. Chem. Theory Comput2020161411.] linked the acceleration factor of the dynamics to the loss of geometric information upon coarse-graining. Our hypothesis is that coarse-graining a multiatom molecule or group into a single spherical bead smooths the molecular surface and, thus, leads to reduced intermolecular friction. A key parameter is the molecular roughness difference, which is calculated via a numerical comparison of the molecular surfaces of both the atomistic and coarse-grained models. Augmenting the RoughMob method, we add the concept of the region where the roughness acts. This information is contained in four so-called roughness volumes. For 17 systems of homogeneous hydrocarbon fluids, simple one-bead coarse-grained models are derived by the structure-based iterative Boltzmann inversion. They include 13 different homogeneous aliphatic and aromatic molecules and two different mapping schemes. We present a simple way to correlate the roughness volumes to the acceleration factor. The resulting relation is able to a priori predict the acceleration factors for an extended size and shape range of hydrocarbon molecules, with different mapping schemes and different densities.