Reparameterization of the mW model to accurately predict the experimental phase diagram of methane hydrate

Due to their high computational efficiency, the coarse-grained water models are of particular importance for practical molecular simulations of gas hydrates. In these models, the mW model is successfully used to study many thermodynamics and dynamics of methane hydrate. Yet, despite several decades...

Full description

Saved in:
Bibliographic Details
Published inThe Journal of chemical physics Vol. 161; no. 17
Main Authors Jin, Dongliang, Zhong, Jing
Format Journal Article
LanguageEnglish
Published United States 07.11.2024
Online AccessGet more information

Cover

Loading…
More Information
Summary:Due to their high computational efficiency, the coarse-grained water models are of particular importance for practical molecular simulations of gas hydrates. In these models, the mW model is successfully used to study many thermodynamics and dynamics of methane hydrate. Yet, despite several decades of intense research, the mW model is still found to overestimate the melting temperature of methane hydrate. We here employ the minimum mean squared error estimation to revisit the key parameter of the mW model, which determines the strength of the tetrahedral angle of the water system. Relying on the free energy calculations, we first estimate the chemical potentials of water in the liquid phase for temperatures at which methane hydrate forms. We then turn to the mean squared error to describe the chemical potential deviation between the mW model and the TIP4P/ice model (the latter could reproduce the experimental phase diagram of methane hydrate). By minimizing the mean squared error, we finally have an optimized parameter for the mW model. In this part, we also discuss the pressure effect on such reparameterization procedure. Moreover, relying on the direct coexistence method, the melting temperature determined using the reparameterized mW model is found to be consistent with the experimental data. This strategy provides a means to improve the coarse-grained model to match the experimental observations for temperatures in the range of interest.
ISSN:1089-7690
DOI:10.1063/5.0228522