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

TL;DR

This study uses machine learning and molecular dynamics to identify structural features in vitreous silica that predict its thermodynamic stability, revealing the importance of medium-range features especially between 2.8-6 Å.

Contribution

It introduces a data-driven approach to link structural descriptors with stability in vitreous silica, highlighting medium-range features as key predictors.

Findings

Medium-range features are most indicative of stability.

A set of five structural features effectively predict silica stability.

Short-range features become less relevant below the fragile-to-strong transition.

Abstract

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.