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This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. The atomistic distributions emulate the local chain structure. In the top-down approach, optimization of nonbonded potentials is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that CG model parameters achieved through our machine-learning enabled hybrid optimization framework fulfills the thermodynamic consistency and transferability issues associated with the classical approaches to coarse-graining model polymers. We demonstrate the efficiency, accuracy, and transferability of the developed CG model, using our novel framework through accurate predictions of chain size as well as chain dynamics, including the limiting behavior of the glass transition temperature, diffusion, and stress relaxation spectrum, where none were included in the potential parameterization process. The accuracy of the predicted properties are evaluated in the context of molecular theories and available experimental data.

In this paper we report a novel inertial instability that occurs in electro-osmotically driven channel flows. We assume that the charge motion under the influence of an externally applied electric field is confined to a small vicinity of the channel walls that, effectively, drives a bulk flow through a prescribed slip velocity at the boundaries. Here, we study spatially-periodic wall velocity modulations in a two-dimensional straight channel numerically. At low slip velocities, the bulk flow consists of a set of vortices along each wall that are left-right symmetric, while at sufficiently high slip velocities, this flow loses its stability though a supercritical bifurcation. Surprisingly, the new flow state that bifurcates from a left-right symmetric base flow has a rather strong mean component along the channel, which is similar to pressure-driven velocity profiles. The instability sets in at rather small Reynolds numbers of about 20-30, and we discuss its potential applications in microfluidic devices., Comment: 13 pages, 6 figures

The flow of dense suspensions, glasses, and granular materials is heavily influenced by frictional interactions between constituent particles. However, neither hydrodynamics nor friction has successfully explained the full range of flow phenomena in concentrated suspensions. Particles with asperities represent a case in point. Lubrication hydrodynamics fail to completely capture two key rheological properties - namely, that the viscosity increases drastically and the first normal stress difference can switch signs as volume fraction increases. Yet, simulations that account for interparticle friction are also unable to fully predict these properties. Furthermore, experiments show that rheological behavior can vary depending on particle roughness and deformability. We seek to resolve these apparent contradictions by systematically tuning the roughness of model colloids, investigating their viscosity and first normal stress differences under steady shear, and finally generating a rheological state diagram that demonstrates how surface roughness influences the transition between shear thickening and dilatancy. Our simulations, which are in good agreement with the experiments, suggest that friction between rough particles is significant. In addition, we find that roughness progressively lowers the critical conditions required for the onset of shear thickening and dilatancy. Our results thus provides a major contribution in the field of suspension rheology with broad relevance to granular and particulate materials. For instance, particle geometry can be tuned to increase the efficacy of materials that turn solid-like on the application of stimuli. On the other hand, engineers who work with concentrated slurries can now use images of the constituent particles to estimate optimal flow processing conditions.

We propose minimal models of one-, two- and three-dimensional micro-swimmers at low Reynolds number with a periodic non-reciprocal motion. These swimmers are either "pushers" or "pullers" of fluid along the swimming axis, or combination of the two, depending on the history of the swimming motion. We show this with a linear three-bead swimmer by analytically evaluating the migration speed and the strength of the dipolar flow induced by its swimming motion. It is found that the distance traveled per cycle and the dipolar flow can be obtained from an integral over the area enclosed by the trajectory of the cycle projected onto a cross-plot of the two distances between beads. Two- and three-dimensional model swimmers can tumble by breaking symmetry of the swimming motion with respect to the swimming axis, as occurs in the tumbling motion of Escherichia coli or Chlamydomonas, which desynchronize the motions of their flagella to reorient the swimming direction. We also propose a five-bead model of a "corkscrew swimmer", i.e. with a helical flagellum and a rotary motor attached to the cell body. Our five-bead swimmer is attracted to a nearby wall, where it swims clockwise as observed in experiments with bacteria with helical flagella.