High Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning

TL;DR

This study uses unsupervised learning on molecular dynamics data to analyze the complex, high-dimensional fluctuations of water's hydrogen bond network, revealing the necessity of combining atomic descriptors with chemical intuition.

Contribution

It introduces a novel framework combining data-driven and chemical insights to characterize water's structural fluctuations across different conditions.

Findings

Fluctuations occur in a high-dimensional space.

A combination of atomic descriptors and chemical coordinates is essential.

The approach is consistent across different water models.

Abstract

The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free energy landscape of water starting from encoding the water environment using local-atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy at ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space which we characterize using a combination of both atomic descriptors and chemical-intuition based coordinates. We demonstrate that a combination of both types of variables are needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length-scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.