MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data.

Molecular dynamics simulation is a method of investigating the behavior of molecules, which is useful for analyzing a variety of structural and dynamic properties and mechanisms of phenomena. However, the huge computational cost of large-scale and long-time simulations is an enduring problem that must be addressed. MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data [Endo , , 2018, ].

Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow.

Monte Carlo molecular simulation is a powerful computational method for simulating molecular behavior. It generates samples of the possible states of molecular systems. To generate a sample efficiently, it is advantageous to avoid suggesting extremely high-energy states that would never become possible states.

Dataflow programming for the analysis of molecular dynamics with AViS, an analysis and visualization software application.

The study of molecular dynamics simulations is largely facilitated by analysis and visualization toolsets. However, these toolsets are often designed for specific use cases and those only, while scripting extensions to such toolsets is often exceedingly complicated. To overcome this problem, we designed a software application called AViS which focuses on the extensibility of analysis.

Anisotropy of dodecahedral water cages for guest gas occupancy in semiclathrate hydrates.

Anisotropic dodecahedral (D) water cages found in semiclathrate hydrates have unique gas selectivity due to their varied shapes. Herein, the D cages incorporating ideally isotropic rare gases, i.e.

Detection of molecular behavior that characterizes systems using a deep learning approach.

Molecular dynamics (MD) simulation is a powerful computational method to observe molecular behavior. Although the detection of molecular behavior that characterizes systems is an important task in the study of MD, it is typically difficult and depends on human expert knowledge. Therefore, we propose a novel analysis scheme for MD data using deep neural networks.

Cage occupancies, lattice constants, and guest chemical potentials for structure II hydrogen clathrate hydrate from Gibbs ensemble Monte Carlo simulations.

In this paper, equilibrium properties of structure II hydrates of hydrogen were determined from Monte Carlo simulations in the isothermal-isobaric Gibbs ensemble. Water and hydrogen molecules are described by the TIP4P/Ice and Silvera-Goldman models, respectively. The use of the Gibbs ensemble has many key advantages for the simulation of hydrates.

Analysis of three-phase equilibrium conditions for methane hydrate by isometric-isothermal molecular dynamics simulations.

To develop prediction methods of three-phase equilibrium (coexistence) conditions of methane hydrate by molecular simulations, we examined the use of NVT (isometric-isothermal) molecular dynamics (MD) simulations. NVT MD simulations of coexisting solid hydrate, liquid water, and vapor methane phases were performed at four different temperatures, namely, 285, 290, 295, and 300 K. NVT simulations do not require complex pressure control schemes in multi-phase systems, and the growth or dissociation of the hydrate phase can lead to significant pressure changes in the approach toward equilibrium conditions.

Nucleation rate analysis of methane hydrate from molecular dynamics simulations.

Clathrate hydrates are solid crystalline structures most commonly formed from solutions that have nucleated to form a mixed solid composed of water and gas. Understanding the mechanism of clathrate hydrate nucleation is essential to grasp the fundamental chemistry of these complex structures and their applications. Molecular dynamics (MD) simulation is an ideal method to study nucleation at the molecular level because the size of the critical nucleus and formation rate occur on the nano scale.