Insights into the mechanical stability of tetrahydrofuran hydrates from experimental, machine learning, and molecular dynamics perspectives
Yan‐Wen Lin, Ziyue Zhou, Zixuan Song, Qiao Shi, Yongchao Hao, Yuequn Fu, Tong Li, Zhisen Zhang, Jianyang Wu
Abstract
cages being predominant. This study enhances our understanding of the mechanical properties and deformation mechanisms of hydrates and provides a ML-based predictive framework for estimating the compressive strength of hydrates under diverse coupling conditions. The findings have significant implications for stability assessments of NGHs and the exploitation of NGH resources.
Topics & Concepts
TetrahydrofuranMolecular dynamicsStability (learning theory)Dynamics (music)Clathrate hydrateMaterials scienceArtificial intelligenceNanotechnologyComputer scienceMachine learningChemistryComputational chemistryPsychologyHydrateOrganic chemistryPedagogySolventMethane Hydrates and Related PhenomenaHydrocarbon exploration and reservoir analysisHydraulic Fracturing and Reservoir Analysis