Establishing Uniform Acceptance in Force Biased Monte Carlo Simulations

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. In this study, we propose a new sampling method for Monte Carlo molecular simulation, that is, a continuous normalizing molecular flow (CNMF) method, which can create various probabilistic distributions of molecular states from some initial distribution. The CNMF method generates samples by solving a first-order differential equation with two-body intermolecular interaction terms. We also develop specific probabilistic distributions using CNMF called inverse square flow, which yields distributions with zero probability density when molecule pairs are in close proximity, whereas probability densities are compressed uniformly from the initial distribution in all other cases. Using inverse square flow, we demonstrate that Monte Carlo molecular simulation is more efficient than the standard simulation. Although the increased computational costs of the CNMF method are non-negligible, this method is feasible for parallel computation and has the potential for expansion.

First principles reactive simulation for equation of state prediction

The high cost of density functional theory (DFT) has hitherto limited the ab initio prediction of the equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction and thus the assessment of high explosives. In particular, we find that DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman-Jouguet state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. We also demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling.

First-Principles Simulations of CuCl in High-Temperature Water Vapor

Experimental data suggest that the solubility of copper in high-temperature water vapor is controlled by the formation of hydrated clusters of the form CuCl(H₂O)_n, where the average number of water molecules in the cluster generally increases with increasing density [Migdisov, A. A.; et al. Geochim. Cosmochim. Acta 2014, 129, 33-53]. However, the precise nature of these clusters is difficult to probe experimentally. Moreover, there are some discrepancies between experimental estimates of average cluster size and prior simulation work [Mei, Y. Geofluids 2018, 2018, 4279124]. We have performed first-principles Monte Carlo (MC) and molecular dynamics (MD) simulations to explore these clusters in finer detail. We find that molecular dynamics is not the most appropriate technique for studying aggregation in vapor phases, even at relatively high temperatures. Specifically, our MD simulations exhibit substantial problems in adequately sampling the equilibrium cluster size distribution. In contrast, MC simulations with specialized cluster moves are able to accurately sample the phase space of hydrogen-bonding vapors. At all densities, we find a stable, slightly distorted linear H₂O-Cu-Cl structure, which is in agreement with the earlier simulations, surrounded by a variable number of water molecules. The surrounding water molecules do not form a well-defined second solvation shell but rather a loose network of hydrogen-bonded water with molecular CuCl on the outside edge of the water cluster. We also find a broad distribution of hydration numbers, especially at higher densities. In contrast to previous simulation work but in agreement with experimental data, we find that the average hydration number substantially increases with increasing density. Moreover, the value of the hydration number depends on the choice of cluster definition.

Atomistic simulations of graphite etching at realistic time scales

Hydrogen-graphite interactions are relevant to a wide variety of applications, ranging from astrophysics to fusion devices and nano-electronics. In order to shed light on these interactions, atomistic simulation using Molecular Dynamics (MD) has been shown to be an invaluable tool. It suffers, however, from severe time-scale limitations. In this work we apply the recently developed Collective Variable-Driven Hyperdynamics (CVHD) method to hydrogen etching of graphite for varying inter-impact times up to a realistic value of 1 ms, which corresponds to a flux of ∼1020 m-2 s-1. The results show that the erosion yield, hydrogen surface coverage and species distribution are significantly affected by the time between impacts. This can be explained by the higher probability of C-C bond breaking due to the prolonged exposure to thermal stress and the subsequent transition from ion- to thermal-induced etching. This latter regime of thermal-induced etching - chemical erosion - is here accessed for the first time using atomistic simulations. In conclusion, this study demonstrates that accounting for long time-scales significantly affects ion bombardment simulations and should not be neglected in a wide range of conditions, in contrast to what is typically assumed.

Atomic scale simulation of carbon nanotube nucleation from hydrocarbon precursors

Atomic scale simulations of the nucleation and growth of carbon nanotubes is essential for understanding their growth mechanism. In spite of over twenty years of simulation efforts in this area, limited progress has so far been made on addressing the role of the hydrocarbon growth precursor. Here we report on atomic scale simulations of cap nucleation of single-walled carbon nanotubes from hydrocarbon precursors. The presented mechanism emphasizes the important role of hydrogen in the nucleation process, and is discussed in relation to previously presented mechanisms. In particular, the role of hydrogen in the appearance of unstable carbon structures during in situ experimental observations as well as the initial stage of multi-walled carbon nanotube growth is discussed. The results are in good agreement with available experimental and quantum-mechanical results, and provide a basic understanding of the incubation and nucleation stages of hydrocarbon-based CNT growth at the atomic level.

Atomic scale simulation of the nucleation and growth of carbon nanotubes is essential for understanding their growth mechanism. Here, the authors look at cap nucleation of nanotubes from hydrocarbon precursors, specifically probing the role of hydrogen in the early stages of growth.

Thermodynamics at the nanoscale: phase diagrams of nickel-carbon nanoclusters and equilibrium constants for phase transitions

Using reactive molecular dynamics simulations, the melting behavior of nickel-carbon nanoclusters is examined. The phase diagrams of icosahedral and Wulff polyhedron clusters are determined using both the Lindemann index and the potential energy. Formulae are derived for calculating the equilibrium constants and the solid and liquid fractions during a phase transition, allowing more rational determination of the melting temperature with respect to the arbitrary Lindemann value. These results give more insight into the properties of nickel-carbon nanoclusters in general and can specifically be very useful for a better understanding of the synthesis of carbon nanotubes using the catalytic chemical vapor deposition method.

Formation of single layer graphene on nickel under far-from-equilibrium high flux conditions

We investigate the theoretical possibility of single layer graphene formation on a nickel surface at different substrate temperatures under far-from-equilibrium high precursor flux conditions, employing state-of-the-art hybrid reactive molecular dynamics/uniform acceptance force bias Monte Carlo simulations. It is predicted that under these conditions, the formation of a single layer graphene-like film may proceed through a combined deposition-segregation mechanism on a nickel substrate, rather than by pure surface segregation as is typically observed for metals with high carbon solubility. At 900 K and above, nearly continuous graphene layers are obtained. These simulations suggest that single layer graphene deposition is theoretically possible on Ni under high flux conditions.