Unsupervised classification of local clathrate hydrate structures

Quantifying and differentiating structural characteristics of clathrate hydrates at the molecular level is crucial for understanding the properties at the heart of hydrate-based technologies. While useful, current approaches lack sufficient resolution to discern, e.g., interfacial and dynamical structures. In this study, we present an algorithm based on Density Peaks Advanced (DPA) clustering, which accurately identifies different states of water co-existing within clathrate hydrates. A key novel component is an effective cavity-finder algorithm, which provides input to the clustering framework. The new algorithm detects hydrate cavities by analyzing the number and type of constituent molecular rings around voids. Integrating the new algorithm with widely used order parameters (e.g., F3, F4, F4t, and displacement) provides a powerful and accurate tool for analyzing hydrate structures at interfaces and phase transitions. The performance of the new algorithm is assessed for structure I (sI) CO2 hydrates and for structure II (sII) mixed CH4/Dioxane hydrates, demonstrating its robustness and adaptability across different clathrates. Crucially, the proposed algorithm allows us to identify partly ordered structures characteristic of the quasi-liquid layer, thus enabling us to capture interfacial dynamics and molecular-scale details essential for understanding hydrates in realistic conditions.