Unveiling the Influence of Water Vacancy Defects on Mechanical Properties of Methane Hydrates: A Molecular Dynamics and Machine Learning Study

The escalating global energy demand and climate challenges underscore the need for sustainable alternatives such as natural gas hydrates (NGHs). However, their mechanical integrity is compromised by ubiquitous structural water vacancies. Here, the mechanical behavior and microscopic structural evolution of methane hydrates containing various water vacancy types and concentrations are investigated using molecular dynamics simulations and machine learning (ML). Results reveal significant mechanical deterioration, with tensile strength, critical strain, and Young's modulus decreasing by up to 42.18%, 10.90%, and 11.83%, respectively, compared to defect‐free hydrates. Even at identical concentrations, mechanical responses variations reach 35.53% depending on defect configuration. Vacancies reduce the initial number of conventional cages by ≈27.1–53.3% and promote the formation of unconventional cages during straining due to disrupted H‐bond networks. The cyclic and interconnected transformations among different cage types highlight the sophisticated molecular‐level self‐adjustment mechanisms of methane hydrates. Gradient boosting decision tree ML models, trained on microstructural features such as cage counts, H‐bonds, radial distribution functions, water vacancy number, and density, accurately predict mechanical properties. This study provides deep insights into the defect‐mediated failure mechanisms of methane hydrates, offering a theoretical foundation for optimizing hydrate exploitation strategies, improving energy recovery efficiency, and mitigating potential geological hazards.