Misc

In this study, we carried out a dissipative particle dynamics simulation about two kind of the length liquid crystal molecules. In this simulation, we observed phases as a volume fraction of liquid crystal molecules as a parameter with temperature change. As a result, we found that a transition temperature from isotropic phase to nematic phase depends on the volume fraction.

A polymer droplet is droplet constructed by polymer chains. The polymer droplet has the great possibility of the application in the industry. However, detailed structure of the polymer droplet have not been yet elucidated under various conditions. In this paper, to investigate behaviors of polymer droplet, we adopted the Many-body Dissipative Particle Dynamics (MDPD) simulation. We measured the radius of droplet and the contact angle, and examined the size effect of polymer droplet system. !

A Janus nanoparticle is a unique anisotropic nanoparticle which typically has two or more distinct surfaces, each with different properties. In the present study, we performed molecular simulations to investigate the morphologies and phase diagrams of self-assembled diblock and triblock Janus nanoparticles confined in nanotubes. We derived qualitative phase diagrams of the the axial pressure versus the ratio of the nanoparticle diameter to that of the nanotube. Two distinct types of nanotube wall, namely hydrophobic and hydrophilic, were considered. Moreover, we applied a shear flow to hydrophobic/hydrophilic these structure and observed change of structure.

We propose a water pump that actively transports water molecules through nanochannels. Spatially asymmetric thermal fluctuations imposed on the channel radius cause unidirectional water flow without osmotic pressure, which can be attributed to hysteresis in the cyclic transition between the wetting/drying states. We show that the water transport depends on fluctuations, such as white, Brownian, and pink noises. Because of the high-frequency components in white noise, fast switching of open and close states inhibits channel wetting. Conversely, pink and Brownian noises generate high-pass filtered net flow. Brownian fluctuation leads to a faster water transport rate, whereas pink noise has a higher capability to overcome osmotic pressure in the opposite direction. A trade-off relationship exists between the resonant frequency of the fluctuation and the flow amplification. The proposed pump can be considered as an analogy for the reversed Carnot cycle, which is the upper limit on the energy conversion efficiency.

Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodynamic properties, and are used as the ML input. However, to accurately find the input--output patterns, ML requires a sufficiently sized dataset that depends on the complexity of the ML model. Generating such a large dataset from MD simulations is not ideal because of their high computation cost. In this study, we present a simple supervised ML method to predict the transport properties of materials. To simplify the model, an unsupervised ML method obtains an efficient representation of molecular movements. This method was applied to predict the viscosity of lubricant molecules in confinement with shear flow. Furthermore, simplicity facilitates the interpretation of the model to understand the molecular mechanics of viscosity. We revealed two types of molecular mechanisms that contribute to low viscosity.