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Understanding phases of water molecules based on local structure is essentialfor understanding their anomalous properties. However, due to complicatedstructural motifs formed via hydrogen bonds, conventional order parametersrepresent the water molecules incompletely. In this paper, we develop aGCIceNet, which automatically generates machine-based order parameters forclassifying the phases of the water molecules via supervised and unsupervisedlearning. Multiple graph convolutional layers in the GCIceNet can learntopological informations of the complex hydrogen bond networks. It shows asubstantial improvement of accuracy for predicting the phase of water moleculesin the bulk system and the ice/vapor interface system. A relative importanceanalysis shows that the GCIceNet can capture the structural features of thegiven system hidden in the input data. Augmented with the vast amount of dataprovided by molecular dynamics simulations, the GCIceNet is expected to serveas a powerful tool for the fields of glassy liquids and hydration layers aroundbiomolecules.

GCIceNet: A Graph Convolutional Network for Accurate Classification of Water Phases