Water’s unique thermophysical properties and how
it mediates
aqueous interactions between solutes have long been interpreted in
terms of its collective molecular structure. The seminal work of Errington
and Debenedetti [Nature
2001, 409, 318–321] revealed a striking hierarchy of relationships
among the thermodynamic, dynamic, and structural properties of water,
motivating many efforts to understand (1) what measures of water structure
are connected to different experimentally accessible macroscopic responses
and (2) how many such structural metrics are adequate to describe
the collective structural behavior of water. Diffusivity constitutes
a particularly interesting experimentally accessible equilibrium property
to investigate such relationships because advanced NMR techniques
allow the measurement of bulk and local water dynamics in nanometer
proximity to molecules and interfaces, suggesting the enticing possibility
of measuring local diffusivities that report on water structure. Here,
we apply statistical learning methods to discover persistent structure–dynamic
correlations across a variety of simulated aqueous mixtures, from
alcohol–water to polypeptoid–water systems. We investigate
a variety of molecular water structure metrics and find that an unsupervised
statistical learning algorithm (namely, sequential feature selection)
identifies only two or three independent structural metrics that are
sufficient to predict water self-diffusivity accurately. Surprisingly,
the translational diffusivity of water across all mixed systems studied
here is strongly correlated with a measure of tetrahedral order given
by water’s triplet angle distribution. We also identify a separate
small number of structural metrics that well predict an important
thermodynamic property, the excess chemical potential of an idealized
methane-sized hydrophobe in water. Ultimately, we offer a Bayesian
method of inferring water structure by using only structure–dynamics
linear regression models with experimental Overhauser dynamic nuclear
polarization (ODNP) measurements of water self-diffusivity. This study
thus quantifies the relationships among several distinct structural
order parameters in water and, through statistical learning, reveals
the potential to leverage molecular structure to predict fundamental
thermophysical properties. In turn, these findings suggest a framework
for solving the inverse problem of inferring water’s molecular
structure using experimental measurements such as ODNP studies that
probe local water properties.