  It is well established that physical aging of amorphous solids is governed by
a marked change in dynamical properties as the material becomes older.
Conversely, structural properties such as the radial distribution function
exhibit only a very weak age dependence, usually deemed negligible with respect
to the numerical noise. Here we demonstrate that the extremely weak
age-dependent changes in structure are in fact sufficient to reliably assess
the age of a glass with the support of machine learning. We employ a supervised
learning method to predict the age of a glass based on the system's
instantaneous radial distribution function. Specifically, we train a multilayer
perceptron for a model glassformer quenched to different temperatures, and find
that this neural network can accurately classify the age of our system across
at least four orders of magnitude in time. Our analysis also reveals which
structural features encode the most useful information. Overall, this work
shows that through the aid of machine learning, a simple structure-dynamics
link can indeed be established for physically aged glasses.
