  Atom probe tomography (APT) is ideally suited to characterize and understand
the interplay of chemical segregation and microstructure in modern
multicomponent materials. Yet, the quantitative analysis typically relies on
human expertise to define regions of interest. We introduce a computationally
efficient, multistage machine learning strategy to identify chemically distinct
domains in a semi automated way, and subsequently quantify their geometric and
compositional characteristics. In our algorithmic pipeline, we first coarse
grain the APT data into voxels, collect the composition statistics, and
decompose it via clustering in composition space. The composition
classification then enables the real space segmentation via a density based
clustering algorithm, thus revealing the microstructure at voxel resolution.
Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two
precipitate phases with a plate-like, but intertwined morphology. The primary
segmentation is further refined to disentangle these geometrically complex
precipitates into individual plate like parts by an unsupervised approach based
on principle component analysis, or a U-Net based semantic segmentation trained
on the former. Following the chemical and geometric analysis, detailed chemical
distribution and segregation effects relative to the predominant plate-like
geometry can be readily mapped without resorting to the initial voxelization.
