论文标题
无监督的学习用于塑料变形的晶体中的结构检测
Unsupervised learning for structure detection in plastically deformed crystals
论文作者
论文摘要
在塑料变形的晶体内检测颗粒尺度的结构，可以更好地理解发生现象。尽管以前的方法主要依赖于在不同的局部参数上应用手选择标准，但这些方法只能检测到已经知道的结构。我们引入了一种无监督的学习算法以在塑性变形下自动检测晶体内的结构。这种方法基于一项用于胶体材料结构检测的研究。该算法具有快速易于实现的计算优势。我们表明，通过基于键角分布的局部参数，我们能够检测到比传统手工制作的标准更高的结构，并且具有更高的精度。
Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different local parameters, these approaches could only detect already known structures.We introduce an unsupervised learning algorithm to automatically detect structures within a crystal under plastic deformation. This approach is based on a study developed for structural detection on colloidal materials. This algorithm has the advantage of being computationally fast and easy to implement. We show that by using local parameters based on bond-angle distributions, we are able to detect more structures and with a higher degree of precision than traditional hand-made criteria.
