  Quantitative analysis of microstructural features on the nanoscale, including
precipitates, local chemical orderings (LCOs) or structural defects (e.g.
stacking faults) plays a pivotal role in understanding the mechanical and
physical responses of engineering materials. Atom probe tomography (APT), known
for its exceptional combination of chemical sensitivity and sub-nanometer
resolution, primarily identifies microstructures through compositional
segregations. However, this fails when there is no significant segregation, as
can be the case for LCOs and stacking faults. Here, we introduce a 3D deep
learning approach, AtomNet, designed to process APT point cloud data at the
single-atom level for nanoscale microstructure extraction, simultaneously
considering compositional and structural information. AtomNet is showcased in
segmenting L12-type nanoprecipitates from the matrix in an AlLiMg alloy,
irrespective of crystallographic orientations, which outperforms previous
methods. AtomNet also allows for 3D imaging of L10-type LCOs in an AuCu alloy,
a challenging task for conventional analysis due to their small size and subtle
compositional differences. Finally, we demonstrate the use of AtomNet for
revealing 2D stacking faults in a Co-based superalloy, without any defected
training data, expanding the capabilities of APT for automated exploration of
hidden microstructures. AtomNet pushes the boundaries of APT analysis, and
holds promise in establishing precise quantitative microstructure-property
relationships across a diverse range of metallic materials.
