  The strengths of glasses are intricately linked to their atomic-level
heterogeneity. Atomistic simulations are frequently used to investigate the
statistical physics of this relationship, compensating for the limited
spatiotemporal resolution in experimental studies. However, theoretical
insights are limited by the complexity of glass structures and the accuracy of
the interatomic potentials used in simulations. Here, we investigate the
strengths and fracture mechanisms of 2D silica, with all structural units
accessible to direct experimental observation. We develop a neural network
force field for fracture (NN-F$^{3}$) based on the deep potential-smooth
edition (DeepPot-SE) framework. Representative atomic structures across
crystals, nanocrystalline, paracrystalline, and continuous random network
glasses are studied. We find that the virials or bond lengths control the
initialization of bond-breaking events, creating nanoscale voids in the
vitreous network. However, the voids do not necessarily lead to crack
propagation due to a disorder-trapping effect, which is stronger than the
lattice-trapping effect in a crystalline lattice and occurs over larger length
and time scales. Fracture initiation proceeds with void growth and coalescence,
and advances through a bridging mechanism. The fracture patterns are shaped by
subsequent trapping and cleavage steps, often guided by voids forming ahead of
the crack tip. These heterogeneous processes result in atomically smooth facets
in crystalline regions and rough, amorphous edges in the glassy phase. These
insights into 2D crystals and glasses, both sharing SiO$_{2}$ chemistry,
highlight the pivotal role of atomic-level structures in determining fracture
kinetics and path selection in materials.
