Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematical modeling approaches to dynamical systems. To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons. We evaluate computational performance of the proposed model in two time series prediction tasks related to four chaotic fast-slow dynamical systems. In a one-step-ahead prediction task where input data are provided only from the fast subsystem, we show that the proposed model yields better performance than the standard RC model with identical LI neurons. Our analysis reveals that the timescale required for producing each component of target multiscale dynamics is appropriately and flexibly selected from the reservoir dynamics by model training. In a long-term prediction task, we demonstrate that a closed-loop version of the proposed model can achieve longer-term predictions compared to the counterpart with identical LI neurons depending on the hyperparameter setting.

Electrokinetic flows of an aqueous NaCl solution in nanochannels with negatively charged surfaces are studied using molecular dynamics simulations. The four transport coefficients that characterize the response to weak electric and pressure fields, namely, the coefficients for the electrical current in response to the electric field (Mjj) and the pressure field (Mjm), and those for the mass flow in response to the same fields (Mmj and Mmm), are obtained in the linear regime using a Green–Kubo approach. Nonequilibrium simulations with explicit external fields are also carried out, and the current and mass flows are directly obtained. The two methods exhibit good agreement even for large external field strengths, and Onsager's reciprocal relation (Mjm = Mmj) is numerically confirmed in both approaches. The influence of the surface charge density on the flow is also considered. The values of the transport coefficients are found to be smaller for larger surface charge density, because the counter-ions strongly bound near the channel surface interfere with the charge and mass flows. A reversal of the streaming current and of the reciprocal electro-osmotic flow, with a change of sign of Mmj due to the excess co-ions, takes places for very high surface charge density.

In this paper, we explore various forms of osmotic transport in the regime of high solute concentration. We consider both the osmosis across membranes and diffusio-osmosis at solid interfaces, driven by solute concentration gradients. We follow a mechanical point of view of osmotic transport, which allows us to gain much insight into the local mechanical balance underlying osmosis. We demonstrate in particular how the general expression of the osmotic pressure for mixtures, as obtained classically from the thermodynamic framework, emerges from the mechanical balance controlling non-equilibrium transport under solute gradients. Expressions for the rejection coefficient of osmosis and the diffusio-osmotic mobilities are accordingly obtained. These results generalize existing ones in the dilute solute regime to mixtures with arbitrary concentrations.

We demonstrate, on the basis of molecular dynamics simulations, the possibility of an efficient water-ethanol separation using nanoporous carbon membranes, namely, carbon nanotube membranes, nanoporous graphene sheets, and multilayer graphene membranes. While these carbon membranes are in general permeable to both pure liquids, they exhibit a counter-intuitive “self-semi-permeability” to water in the presence of water-ethanol mixtures. This originates in a preferred ethanol adsorption in nanoconfinement that prevents water molecules from entering the carbon nanopores. An osmotic pressure is accordingly expressed across the carbon membranes for the water-ethanol mixture, which agrees with the classic van’t Hoff type expression. This suggests a robust and versatile membrane-based separation, built on a pressure-driven reverse-osmosis process across these carbon-based membranes. In particular, the recent development of large-scale “graphene-oxide” like membranes then opens an avenue for a versatile and efficient ethanol dehydration using this separation process, with possible application for bio-ethanol fabrication.