Novel approach for designing order parameters of clathrate hydrate structures by graph neural network

Clathrate hydrates continue to be the focus of active research efforts due to their use in energy resources, transportation, and storage-related applications. Therefore, it is crucial to define their essential characteristics from a molecular standpoint. Understanding molecular structure in particular is crucial because it aids in understanding the mechanisms that lead to the formation or dissociation of clathrate hydrates. In the past, a wide variety of order parameters have been employed to classify and evaluate hydrate structures. An alternative approach to inventing bespoke order parameters is to apply machine learning techniques to automatically generate effective order parameters. In earlier work, we suggested a method for automatically designing novel parameters for ice and liquid water structures with Graph Neural Networks (GNNs). In this work, we use a GNN to implement our method, which can independently produce feature representations of the molecular structures. By using the TeaNet-type model in our method, it is possible to directly learn the molecular geometry and topology. This enables us to build novel parameters without prior knowledge of suitable order parameters for the structure type, discover structural differences, and classify molecular structures with high accuracy. We use this approach to classify the structures of clathrate hydrate structures: sI, sII, and sH. This innovative approach provides an appealing and highly accurate replacement for the traditional order parameters. Furthermore, our method makes clear the process of automatically designing a universal parameter for liquid water, ice, and clathrate hydrate to analyze their structures and phases.