Machine learning potential insights into mechanical response and heat transfer in CO₂ hydrate

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.