GCIceNet：一种用于水相精确分类的图卷积网络
GCIceNet: A Graph Convolutional Network for Accurate Classification of Water Phases
摘要：
基于局域结构理解水分子的相对于理解其反常性质是至关重要的。然而，由于通过氢键形成的复杂的结构基序，传统的序参量表示水分子不完全。在本文中，我们开发了一个GCIceNet，它自动生成基于机器的序参量，通过监督和无监督学习对水分子的相进行分类。GCICeNet中的多个图卷积层可以学习复杂氢键网络的拓扑信息。它显示了一个实质性的改进，预测水分子在散装系统和冰/蒸汽界面系统的相的准确性。一个相对重要性分析表明，GCIceNet可以捕捉到隐藏在输入数据中的给定系统的结构特征。通过分子动力学模拟提供的大量数据，GCIceNet有望成为生物分子周围玻璃状液体和水合层领域的有力工具。
英文摘要：
Understanding phases of water molecules based on local structure is essential for understanding their anomalous properties. However, due to complicated structural motifs formed via hydrogen bonds, conventional order parameters represent the water molecules incompletely. In this paper, we develop a GCIceNet, which automatically generates machine-based order parameters for classifying the phases of the water molecules via supervised and unsupervised learning. Multiple graph convolutional layers in the GCIceNet can learn topological informations of the complex hydrogen bond networks. It shows a substantial improvement of accuracy for predicting the phase of water molecules in the bulk system and the ice/vapor interface system. A relative importance analysis shows that the GCIceNet can capture the structural features of the given system hidden in the input data. Augmented with the vast amount of data provided by molecular dynamics simulations, the GCIceNet is expected to serve as a powerful tool for the fields of glassy liquids and hydration layers around biomolecules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
