The technique of atom probe tomography is often used to image solute clusters and solute atom segregation to dislocation lines in structural alloys. Quantitative analysis, however, remains a common challenge. To address this gap, we combined a cluster finding algorithm, a skeleton finder algorithm, and morphological classification of dense objects to distinguish solute clusters from solute-decorated dislocation lines, both being characterized by high solute atom densities. The proposed workflow is packaged into a graphical user interface available through GitHub. We illustrate its application on a synthetic dataset containing known objects and apply it to an experimental dataset obtained from a proton-irradiated Alloy 625 that contains high densities of Si-decorated dislocations and Si-rich clusters.

中文翻译：

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原子探针断层扫描数据中密集物体的形态分类

原子探针断层扫描技术通常用于将溶质簇和溶质原子偏析到结构合金中的位错线成像。然而，定量分析仍然是一个共同的挑战。为了解决这一差距，我们结合了聚类查找算法、骨架查找算法和密集物体的形态分类，以将溶质簇与溶质修饰的位错线区分开来，两者都以高溶质原子密度为特征。建议的工作流程被打包成一个图形用户界面，可通过 GitHub 获得。我们说明了它在包含已知物体的合成数据集上的应用，并将其应用于从质子辐照合金 625 获得的实验数据集，该合金包含高密度的 Si 装饰位错和富 Si 簇。