Local Order Parameters Classifying Water Networks of Ice and Cyclopropane Clathrate Hydrates

Clathrate hydratesare crystalline solids with guest moleculesin a hydrogen-bonding network of frozen water. Cyclopropane hydratehas a four-phase coexistence point of guest gas, ice, and two hydrates,suggesting ice-hydrate and hydrate-hydrate structuraltransitions, but little is known about the details. Microscopic approachessuch as molecular simulations are expected as a means of elucidation,but these require reaction coordinates that are sensitive to structuralchanges (e.g., order parameters). Here, 1220 local order parameters(LOPs) and their combinations were used to classify cyclopropane hydrateand ice structures in the vicinity of the four-phase coexistence point.A total of 21 599 490 LOP combinations were automaticallyand systematically evaluated via supervised machine learning. Theoptimal (set of) LOP(s) distinguished between structure I and structureII cyclopropane hydrates and ice Ih with high accuracy and robustness. An exhaustive search for local orderparameters (LOPs) usingMolecular Assembly structure Learning package for Identifying Orderparameters (MALIO) revealed a single or a combination of two LOPsthat distinguish three types of water networks: ice Ih, structureI hydrate, and structure II hydrate.

Automated summary

This study combines molecular simulation and machine learning to successfully investigate the structural transitions of cyclopropane hydrate and ice. By evaluating and classifying different local order parameters, the optimal set of order parameters that distinguish between different structures is determined.