Grain boundaries (GBs) in natural gas hydrates (NGHs) are ubiquitous crystal imperfection that undermine the stability of NGHs. In this study, we report the diverse cage structures and mechanical properties of GBs in methane hydrates using molecular dynamic (MD) simulations with advanced machine learning (ML) techniques. Employing a Random Forest-based feature extraction method, we weight and rank the 84 degrees of freedom to eliminate cage types with zero weight. Notably, 11 cage types identified through principal component analysis (PCA) play critical roles in dictating the overall mechanical properties of GBs in methane hydrates. This work establishes an MD-ML framework to elucidate the relationships between GB composition-structure-mechanical properties in NGHs, which is of great importance in predicting the mechanical stability of sediment-hosted NGHs.

Accurate prediction of the mechanical and thermal properties of CO₂ hydrates is essential for their applications in carbon sequestration and refrigeration, yet remains challenging with empirical forcefields. In this work, a deep potential machine learning potential for CO₂ hydrate, trained on density functional theory datasets, is for the first time developed to serve as a unified and accurate computational framework. The as-developed deep potential machine learning potential achieves near-density functional theory accuracy in energy, force, and virial stress predictions while enabling large-scale molecular dynamics simulations at significantly reduced computational cost. Uniaxial stress-strain analyses demonstrate that the model captures the tensile strength and progressive ductile-like failure behavior. Thermal conductivity prediction agrees closely with experimental measurements within 2% deviation, outperforming empirical forcefields. Vibrational dynamics and phonon analyses reveal that the deep potential machine learning potential more accurately describes the anharmonicity and phonon scattering, especially in high-frequency modes, yielding physically realistic thermal transport behavior. This work establishes deep potential machine learning potential as a robust tool for advancing CO₂ hydrate-based technologies by providing a path for accurate and efficient multi-property prediction.

Gas clathrate hydrates show promising applications in sustainable technologies such as future energy resources, gas capture and storage. The stability of clathrate hydrates under external load is of great crucial to those important applications, but remains unknown. Water vacancy is a common structural defect in clathrate hydrates. Herein, the mechanical characteristics of sI methane hydrates containing three types of water vacancy are investigated by molecular dynamics simulations with four different water forcefields. Mechanical properties of methane hydrates such as tensile strength are dictated not only by the density but also the type of water vacancy. Surprisingly, the tensile strength of methane hydrates can be weakened or strengthened, depending on the adopted water model and water vacancy density. Strength enhancement mainly results from the formation of new water cages. This work provides critical insights into the mechanics and microstructural properties of methane clathrate hydrates under external load, which is of primary importance in the recovery of natural gas from methane hydrate reservoirs.