Collective motion of dislocations is governed by the obstacles they encounter. In pure crystals, dislocations form complex structures as they become jammed by their anisotropic shear stress fields. On the other hand, introducing disorder to the crystal causes dislocations to pin to these impeding elements and, thus, leads to a competition between dislocation-dislocation and dislocation-disorder interactions. Previous studies have shown that, depending on the dominating interaction, the mechanical response and the way the crystal yields change.Here we employ three-dimensional discrete dislocation dynamics simulations with varying density of fully coherent precipitates to study this phase transition − from jamming to pinning − using unsupervised machine learning. By constructing descriptors characterizing the evolving dislocation configurations during constant loading, a confusion algorithm is shown to be able to distinguish the systems into two separate phases. These phases agree well with the observed changes in the relaxation rate during the loading. Our results also give insights on the structure of the dislocation networks in the two phases.

中文翻译：

---

通过机器学习探索从位错阻塞到固定的转变

脱位的集体运动受其遇到的障碍支配。在纯晶体中，位错会因其各向异性的剪切应力场而被堵塞，从而形成复杂的结构。另一方面，向晶体中引入无序会导致位错固定在这些阻碍元素上，从而导致位错-位错与位错-无序相互作用之间的竞争。先前的研究表明，取决于主要的相互作用，机械响应和晶体产率的变化方式，在此我们使用三维离散位错动力学模拟方法，对完全相干析出物的密度进行变化，以研究这种相变-从堵塞到钉扎−使用无监督机器学习。通过构造描述恒定负载过程中位错演化构型的描述符，显示了一种混淆算法，能够将系统分为两个独立的阶段。这些阶段与加载过程中观察到的弛豫率变化非常吻合。我们的研究结果还提供了两个阶段中位错网络结构的见解。