Pressure-Induced Stability of Methane Hydrate from Machine Learning Force Field Simulations

Abstract

The stability of methane hydrate is essential for its practical applications as an energy resource, but the stability mechanism of methane hydrate under pressure has not been clarified at the molecular level. Here, we report the effect of pressure on the stability and decomposition of methane hydrate from molecular dynamics simulations using a quantum mechanics-derived machine-learning force field. We find that the decomposition behavior of methane hydrate is dominated by regulating the stability of the water cages. The water molecules have the largest stable amplitude when the distance of water molecules in cages is reduced at a 2 kbar pressure in which the distance is close to that in ice. Therefore, the most stable methane hydrate occurs at 2 kbar. In contrast, too low or too high pressure causes the distance of water molecules to deviate from the optimal value, which leads to the rapid deconstruction of the water cages and promotes the decomposition of methane hydrates. These findings provide molecular insight into the pressuredependent decomposition of methane hydrates and may be extended to the stability of other gas hydrates under pressure.

Review of Heat Transfer Characteristics of Natural Gas Hydrate

“…It mainly uses algorithms and statistical models to complete complex tasks through big data statistics and predict macroscopic physical properties. It has been applied to the study of natural gas hydrate formation and exploitation [107][108][109]. Therefore, in the future, machine learning and other means can be used in the study of multi-phase heat transfer and multi-phase seepage in the hydrate mining process.…”