用机器学习方法探讨位错干扰到钉住的转变

Henri Salmenjoki, Lasse Laurson, Mikko J. Alava
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引用次数: 6

摘要

错位的集体运动是由它们遇到的障碍决定的。在纯晶体中,当位错被各向异性的剪切应力场堵塞时,它们会形成复杂的结构。另一方面,在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-位错和位错-无序相互作用之间的竞争。先前的研究表明,根据主要的相互作用,机械反应和晶体产量的方式会发生变化。在这里,我们采用三维离散位错动力学模拟与不同密度的全共相沉淀来研究这种相变。从卡到钉?使用无监督机器学习。通过构造描述恒加载过程中不断变化的位错结构的描述符,证明了一种混淆算法能够将系统区分为两个独立的阶段。这些相与加载过程中所观察到的弛豫速率变化相吻合。我们的研究结果也对两相中位错网络的结构提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Probing the transition from dislocation jamming to pinning by machine learning

Probing the transition from dislocation jamming to pinning by machine learning

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.

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期刊介绍: Journal of Materials Science: Materials Theory publishes all areas of theoretical materials science and related computational methods. The scope covers mechanical, physical and chemical problems in metals and alloys, ceramics, polymers, functional and biological materials at all scales and addresses the structure, synthesis and properties of materials. Proposing novel theoretical concepts, models, and/or mathematical and computational formalisms to advance state-of-the-art technology is critical for submission to the Journal of Materials Science: Materials Theory. The journal highly encourages contributions focusing on data-driven research, materials informatics, and the integration of theory and data analysis as new ways to predict, design, and conceptualize materials behavior.
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