Unsupervised Learning of the Structure and Dynamics of Liquid Water

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 is 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 is 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. We also explore the collective nature of orientational fluctuations on the free energy landscape. Specifically, we develop an unsupervised protocol for identifying reorientational dynamics in liquid water. We show that large swings are more likely to occur higher up in the free energy landscape than smaller amplitude swings. We show that these orientational fluctuations are collective and occur in waves on the order of tens of picoseconds. These waves of large swings are found to correlate well with the fraction of defects as well as the fluctuations in local density