Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning.

Thin films of amorphous alumina (a-AlO) have recently been found to deform permanently up to 100% elongation without fracture at room temperature. If the underlying ductile deformation mechanism can be understood at the nanoscale and exploited in bulk samples, it could help to facilitate the design of damage-tolerant glassy materials, the holy grail within glass science. Here, based on atomistic simulations and classification-based machine learning, we reveal that the propensity of a-AlO to exhibit nanoscale ductility is encoded in its static (nonstrained) structure. By considering the fracture response of a series of a-AlO systems quenched under varying pressure, we demonstrate that the degree of nanoductility is correlated with the number of bond switching events, specifically the fraction of 5- and 6-fold coordinated Al atoms, which are able to decrease their coordination numbers under stress. In turn, we find that the tendency for bond switching can be predicted based on a nonintuitive structural descriptor calculated based on the static structure, namely, the recently developed "softness" metric as determined from machine learning. Importantly, the softness metric is here trained from the spontaneous dynamics of the system (.., under zero strain) but, interestingly, is able to readily predict the fracture behavior of the glass (.., under strain). That is, lower softness facilitates Al bond switching and the local accumulation of high-softness regions leads to rapid crack propagation. These results are helpful for designing glass formulations with improved resistance to fracture.