The ability of the water molecules to form complex hydrogen-bonded structures results in the formation of cages called hosts that can fit up the other small or large gas molecules, for example, methane in our case, also known as guests within it. Such structures are crystalline in nature and are termed as natural gas hydrates. With the advances in computational techniques and the increase in computational power, it is now possible to simulate such systems with higher accuracy. However, it is difficult to understand the mechanism of nucleation using molecular dynamics (MD) simulations. Furthermore, it is also important to identify crystal polymorphs for controlling nucleation by understanding their thermodynamic stabilities and kinetic properties, as it aids in designing strategies to manipulate nucleation and characterizing synthesized materials. Many algorithms were proposed that can identify ice [1] or hydrates, or both [2] but failed to differentiate among their polymorphs. We propose a supervised machine learning method to analyze the local structure around a central atom and classify different crystal polymorphs of water in the presence of natural gas (methane), for example, natural gas hydrates, ice, and water during the nucleation of gas hydrates. The molecular arrangements seem to be better understood by computing and analyzing bond order parameters, which have been proven to be efficient in distinguishing different structures. In this work, we identify crystal polymorphs of natural gas hydrates by first simulating different structures using molecular dynamics simulation and then employing the machine learning algorithm that uses different sets of order parameters for given crystal polymorphs.
