Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline materials at the nanoscale. In this work, we propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm that clusters atoms into individual grains based on their orientation. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The working of the algorithm is demonstrated on a 122 grain nanocrystalline thin film sample in both undeformed and deformed states. The Orisodata algorithm is also compared with two other grain segmentation algorithms available in the open-source visualization tool Ovito. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries. The model parameters have intuitive physical meaning and relate to similar thresholds used in experiments, which not only helps obtain optimal values but also facilitates easy interpretation and validation of results.
翻译:原子模拟现已成为了解原子规模材料变形机制的一个不可或缺的工具。 大规模模拟经常用于研究纳米尺度多晶素材料的行为。 在这项工作中,我们提出一种方法,使用一种未经监督的机器学习算法,将原子分组成以其方向为基础的个别粒子,对原子结构进行粒分解。 提议的方法称为Orisodata算法,以迭代自我组织的数据分析技术为基础,并被修改为定向空间的工作。 算法的工作在一个122颗粒纳米晶素薄薄膜样本上演示,在非畸形和畸形状态中进行演示。 Orisodata算法也与公开源可视化工具Ovito中的其他两种谷物分解算法进行了比较。 结果表明,Orisodata算法能够正确识别低角粒子界限的双胞体畸形区域。 模型参数具有直观的物理意义,并且与实验中使用的类似临界值有关,这不仅有助于获得最佳值,而且还便于解释和验证结果的简单化。