An EQT-based cDFT approach for thermodynamic properties of confined fluid mixtures

Deep learning-based quasi-continuum theory for structure of confined fluids

Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nano channel. The deep learning model is built based on a convolutional encoder-decoder network (CED) and is applied for high dimensional surrogate modeling to relate the fluid properties to the fluid-fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. We show that the DL-QT model exhibits a robust predictive performance for a confined LJ fluid under various thermodynamic states and water confined in a nanochannel of different widths. The DL-QT model seamlessly connects the molecular physics at nanoscale with the continuum theory by using the deep learning model.

Confinement-Induced Enhancement of Parallel Dielectric Permittivity: Super Permittivity Under Extreme Confinement

Enhancement of parallel (x-y plane) dielectric permittivity of confined fluids has been shown previously. However, a theoretical model that explains this enhancement is lacking thus far. In this study, using statistical-mechanical theories and molecular dynamics simulations, we show an explicit relation between the parallel dielectric permittivity, density variations, and dipolar correlations for protic and aprotic fluids confined in slit-like channels. We analyze the importance of dipolar correlations on enhancement of parallel dielectric permittivity inside large channels and extreme confinements. In large channels, beyond the interfacial region, dipolar correlations exhibit bulk-like behavior. Under extreme confinement, the correlations become stronger to the extent that they give rise to a giant increase in the parallel dielectric permittivity. This sudden increase in dielectric permittivity can be a signature of a liquid transition into higher-ordered structures and has important consequences for understanding ion transport, molecular dissociation, and chemical reactions inside nanoconfined environments.

Test-area surface tension calculation of the graphene-methane interface: Fluctuations and commensurability

The surface tension (γ) of methane on a graphene monolayer is calculated by using the test-area approach. By using a united atom model to describe methane molecules, strong fluctuations of surface tension as a function of the surface area of the graphene are evidenced. In contrast with the liquid-vapor interfaces, the use of a larger cutoff does not fully erase the fluctuations in the surface tension. Counterintuitively, the description of methane and graphene from the Optimized Potentials for Liquid Simulations all-atom model and a flexible model, respectively, led to a lessening in the surface tension fluctuations. This result suggests that the origin of fluctuations in γ is due to a model-effect rather than size-effects. We show that the molecular origin of these fluctuations is the result of a commensurable organization between both graphene and methane. This commensurable structure can be avoided by describing methane and graphene from a flexible force field. Although differences in γ with respect to the model have been often reported, it is the first time that the model drastically affects the physics of a system.