Molecular dynamics simulation is an indispensable tool
for understanding
the collective behavior of atoms and molecules and the phases they
form. Statistical mechanics provides accurate routes for predicting
macroscopic properties as time-averages over visited molecular configurations
- microstates. However, to obtain convergence, we need a sufficiently
long record of visited microstates, which translates to the high-computational
cost of the molecular simulations. In this work, we show how to use
a point cloud-based deep learning strategy to rapidly predict the
structural properties of liquids from a single molecular configuration.
We tested our approach using three homogeneous liquids with progressively
more complex entities and interactions: Ar, NO, and H2O
under varying pressure and temperature conditions within the liquid
state domain. Our deep neural network architecture allows rapid insight
into the liquid structure, here probed by the radial distribution
function, and can be used with molecular/atomistic configurations
generated by either simulation, first-principle, or experimental methods.