The diversity of ice polymorphs is of interest in condensed-matter physics, engineering, astronomy, and biosphere and climate studies. In particular, their triple points are critical to elucidate the formation of each phase and transitions among phases. However, an approach to distinguish their molecular structures is lacking. When precise molecular geometries are given, order parameters are often computed to quantify the degree of structural ordering and to classify the structures. Many order parameters have been developed for specific or multiple purposes, but their capabilities have not been exhaustively investigated for distinguishing ice polymorphs. Here, 493 order parameters and their combinations are considered for two triple points involving the ice polymorphs ice III-V-liquid and ice V-VI-liquid. Supervised machine learning helps automatic and systematic searching of the parameters. For each triple point, the best set of two order parameters was found that distinguishes three structures with high accuracy. A set of three order parameters is also suggested for better accuracy.

中文翻译：

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搜索局部序参数以对三点处的水结构进行分类

冰多晶型的多样性在凝聚态物理、工程、天文学、生物圈和气候研究中很受关注。特别是，它们的三相点对于阐明每个相的形成和相间的转变至关重要。然而，缺乏区分它们的分子结构的方法。当给出精确的分子几何形状时，通常计算顺序参数以量化结构排序的程度并对结构进行分类。许多顺序参数已被开发用于特定或多种目的，但尚未对它们用于区分冰多晶型物的能力进行详尽研究。在这里，对于涉及冰多晶型冰 III-V-液体和冰 V-VI-液体的两个三相点，考虑了 493 个阶参数及其组合。有监督的机器学习有助于自动和系统地搜索参数。对于每个三点，找到了可以高精度区分三种结构的最佳二阶参数集。还建议使用一组三阶参数以获得更好的准确性。