@article{Xiong2025, 
author = {Kaibin Xiong and Yuan Li and Ziyan Lin and Gaoyang Luo and Jianyang Wu},
title = {Machine learning potential insights into mechanical response and heat transfer in CO2 hydrate},
year = {2025},
journal = {Advances in Geo-Energy Research},
volume = {18},
number = {1},
pages = {38-50},
keywords = {CO2 hydrate, mechanical properties, thermal conductivity, deep potential, molecular dynamics},
url = {https://www.sciopen.com/article/10.46690/ager.2025.10.04},
doi = {10.46690/ager.2025.10.04},
abstract = {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.}
}