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The structure-thermodynamic stability relationship in vitreous silica isinvestigated using machine learning and a library of 24,157 inherent structuresgenerated from melt-quenching and replica exchange molecular dynamicssimulations. We find the thermodynamic stability, i.e., enthalpy of theinherent structure ($e_{\mathrm{IS}}$), can be accurately predicted by bothlinear and nonlinear machine learning models from numeric structuraldescriptors commonly used to characterize disordered structures. We findshort-range features become less indicative of thermodynamic stability belowthe fragile-to-strong transition. On the other hand, medium-range features,especially those between 2.8-~6 $\unicode{x212B}$;, show consistentcorrelations with $e_{\mathrm{IS}}$ across the liquid and glass regions, andare found to be the most critical to stability prediction among features fromdifferent length scales. Based on the machine learning models, a set of fivestructural features that are the most predictive of the silica glass stabilityis identified.

Structural Signatures for Thermodynamic Stability in Vitreous Silica: Insight from Machine Learning and Molecular Dynamics Simulations