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

Abstract

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.

First-Principles Performance Prediction of High Explosives Enabled by Machine Learning

“…In NMC, a computationally cheap reference potential is used to accelerate sampling of the “full” (and more computationally expensive) potential. NMC has been widely used and reported on in the literature. − We have recently developed an NMC simulation methodology that leverages ML potentials as the nested reference potential with DFT as the full potential, and due to employing the NMC technique, we retain DFT accuracy exactly . In DFT simulations performed with either Monte Carlo (MC) or molecular dynamics (MD), significant computational time is expended evaluating the energies and forces for structural configurations that have high degrees of similarity.…”