  The structure-thermodynamic stability relationship in vitreous silica is
investigated using machine learning and a library of 24,157 inherent structures
generated from melt-quenching and replica exchange molecular dynamics
simulations. We find the thermodynamic stability, i.e., enthalpy of the
inherent structure ($e_{\mathrm{IS}}$), can be accurately predicted by both
linear and nonlinear machine learning models from numeric structural
descriptors commonly used to characterize disordered structures. We find
short-range features become less indicative of thermodynamic stability below
the fragile-to-strong transition. On the other hand, medium-range features,
especially those between 2.8-~6 $\unicode{x212B}$;, show consistent
correlations with $e_{\mathrm{IS}}$ across the liquid and glass regions, and
are found to be the most critical to stability prediction among features from
different length scales. Based on the machine learning models, a set of five
structural features that are the most predictive of the silica glass stability
is identified.
