论文标题
通过机器学习探测从位错干线到固定的过渡
Probing the transition from dislocation jamming to pinning by machine learning
论文作者
论文摘要
脱位的集体运动受其遇到的障碍的控制。在纯晶体中，位错在其各向异性剪切应力场堵塞时形成复杂的结构。另一方面，将疾病引入晶体会导致错位将这些障碍物固定在这些障碍元素上，从而导致脱位 - 脱位与脱位 - 脱位相互作用之间的竞争。先前的研究表明，取决于主导相互作用，机械响应以及晶体产生的变化方式。 在这里，我们采用三维离散的脱位动力学模拟，使用无需监督的机器学习，从堵塞到固定$ - $的完全相干沉淀的密度不同。通过构造表征恒定加载过程中不断发展的脱位配置的描述符，令人困惑的算法被证明能够将系统区分为两个单独的阶段。这些阶段与观察到的加载过程中观察到的松弛率变化非常吻合。我们的结果还可以深入了解两个阶段中错位网络的结构。
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.
