论文标题
使用Ripley的K功能来表征三维点模式的聚类，并在原子探针断层扫描中进行案例研究
Using Ripley's K-function to Characterize Clustering In 3-Dimensional Point Patterns With a Case Study in Atom Probe Tomography
论文作者
论文摘要
空间分子和原子聚类的大小和结构可以显着影响材料特性，因此对于准确量化很重要。 Ripley的K-功能（K（r））是一种空间相关性的度量，可以使用当感兴趣的物料系统表示为明显的点模式时，可用于执行这种定量。这项工作演示了基于K（r）衍生指标的机器学习模型如何准确估计群集大小和群集内密度中的群集中密度，其中包含不同大小的球形簇的三维（3D）点模式；超过90％的群集大小和群集内密度的模型估计分别在11％和18％的真实值误差范围内。然后将基于K（R）的大小和密度估计值应用于实验性易于重建，以表征7000系列铝合金中的MGZN簇。我们发现，估计值比流行的最大分离算法的估计值更准确，一致且与用户交互。使用K（R）和机器学习来测量聚类是量化此重要材料属性的准确且可重复的方法。
The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley's K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K(r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K(r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. We find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K(r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.
