Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning
Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning
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DOI：
10.1021/acsnano.1c05619
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发表时间：
2021-11-23
期刊：
影响因子：
17.1
通讯作者：
Smedskjaer, Morten M.
中科院分区：
文献类型：
--
作者：
Du, Tao;Liu, Han;Smedskjaer, Morten M.
最近发现无定形氧化铝（a-Al 2 O3）薄膜在室温下永久变形至100%伸长率而不断裂。如果潜在的韧性变形机制可以在纳米尺度上理解并在大块样品中利用，它可以帮助促进耐损伤玻璃材料的设计，这是玻璃科学中的圣杯。在这里，基于原子模拟和基于分类的机器学习，我们揭示了α-Al 2 O3表现出纳米级延展性的倾向是编码在其静态（非应变）结构。通过考虑一系列的a-Al 2 O3系统在不同的压力下淬火的断裂响应，我们证明了纳米延展性的程度与键切换事件的数量相关，特别是5-和6-倍协调的Al原子的分数，这是能够减少其协调数在应力下。反过来，我们发现，键切换的趋势可以预测基于非直观的结构描述符计算的基础上的静态结构，即，最近开发的“软”度量确定从机器学习。重要的是，这里从系统的自发动态（即，在零应变下），但有趣的是，能够容易地预测玻璃的断裂行为（即，在应变下）。也就是说，较低的软度有利于Al键切换和高软度区域的局部积累导致快速裂纹扩展。这些结果有助于设计具有改进的抗断裂性的玻璃配方。
Thin films of amorphous alumina (a-Al2O3) 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-Al2O3 to exhibit nanoscale ductility is encoded in its static (nonstrained) structure. By considering the fracture response of a series of a-Al2O3 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 (i.e., under zero strain) but, interestingly, is able to readily predict the fracture behavior of the glass (i.e., 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.