Unveiling two types of local order in liquid water using machine learning

TL;DR

This paper demonstrates how machine learning can identify two types of local order in liquid water, supporting the hypothesis of two distinct liquid phases with different densities, by combining data preparation with physical insights.

Contribution

It introduces two machine learning methods that recognize local order in liquid water, emphasizing the importance of physical data preparation for successful analysis.

Findings

Machine learning can detect local order in liquid water.

Two types of local molecular order are identified.

Physical data preparation enhances machine learning effectiveness.

Abstract

Machine learning methods are being explored in many areas of science, with the aim of finding solution to problems that evade traditional scientific approaches due to their complexity. In general, an order parameter capable of identifying two different phases of matter separated by a correspond- ing phase transition is constructed based on symmetry arguments. This parameter measures the degree of order as the phase transition proceeds. However, when the two distinct phases are highly disordered it is not trivial to identify broken symmetries with which to find an order parameter. This poses an excellent problem to be addressed using machine learning procedures. Room tem- perature liquid water is hypothesized to be a supercritical liquid, with fluctuations of two different molecular orders associated to two parent liquid phases, one with high density and another one with low density. The validity of this hypothesis is linked to the existence of an order parameter capable of identifying the two distinct liquid phases and their fluctuations. In this work we show how two different machine learning procedures are capable of recognizing local order in liquid water. We argue that when in order to learn relevant features from this complexity, an initial, physically motivated preparation of the available data is as important as the quality of the data set, and that machine learning can become a successful analysis tool only when coupled to high level physical information.