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Searching for local order parameters to classify water structures at triple points
Author(s) -
Doi Hideo,
Takahashi Kazuaki Z.,
Aoyagi Takeshi
Publication year - 2021
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26707
Subject(s) - triple point , set (abstract data type) , order (exchange) , liquid water , point (geometry) , computer science , statistical physics , algorithm , physics , mathematics , thermodynamics , geometry , finance , economics , programming language
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.