Predicting the Fracture Propensity of Amorphous Silica Using Molecular Dynamics Simulations and Machine Learning

TL;DR

This study combines molecular dynamics simulations and machine learning to predict fracture behavior in amorphous silica, revealing how local atomic structures influence ductility and fracture mechanisms.

Contribution

It introduces a novel approach integrating simulations and machine learning to link initial atomic configurations with fracture propensity in amorphous silica.

Findings

Densification increases silica's ductility and toughness.

Changes in local bonding topologies facilitate energy dissipation.

Initial atomic structures encode fracture propensity.

Abstract

Amorphous silica ($a-SiO_2$) is a widely used inorganic material. Interestingly, the relationship between the local atomic structures of $a-SiO_2$ and their effects on ductility and fracture is seldom explored. Here, we combine large-scale molecular dynamics simulations and machine learning methods to examine the molecular deformations and fracture mechanisms of $a-SiO_2$. By quenching at high pressures, we demonstrate that densifying $a-SiO_2$ increases the ductility and toughness. Through theoretical analysis and simulation results, we find that changes in local bonding topologies greatly facilitate energy dissipation during plastic deformation, particularly if the coordination numbers decrease. The appearance of fracture can then be accurately located based on the spatial distribution of the atoms. We further observe that the static unstrained structure encodes the propensity for local atomic coordination to change during applied strain, hence a distinct connection can be made between the initial atomic configurations before loading and the final far-from-equilibrium atomic configurations upon fracture. These results are essential for understanding how atomic arrangements strongly influence the mechanical properties and structural features in amorphous solids and will be useful in atomistic design of functional materials.