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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 learningmethods 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 analysisand simulation results, we find that changes in local bonding topologiesgreatly facilitate energy dissipation during plastic deformation, particularlyif the coordination numbers decrease. The appearance of fracture can then beaccurately located based on the spatial distribution of the atoms. We furtherobserve that the static unstrained structure encodes the propensity for localatomic coordination to change during applied strain, hence a distinctconnection can be made between the initial atomic configurations before loadingand the final far-from-equilibrium atomic configurations upon fracture. Theseresults are essential for understanding how atomic arrangements stronglyinfluence the mechanical properties and structural features in amorphous solidsand will be useful in atomistic design of functional materials.

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