  Nanoscale L12-type ordered structures are widely used in face-centred cubic
(FCC) alloys to exploit their hardening capacity and thereby improve mechanical
properties. These fine-scale particles are typically fully coherent with matrix
with the same atomic configuration disregarding chemical species, which makes
them challenging to be characterized. Spatial distribution maps (SDMs) are used
to probe local order by interrogating the three-dimensional (3D) distribution
of atoms within reconstructed atom probe tomography (APT) data. However, it is
almost impossible to manually analyse the complete point cloud ($>10$ million)
in search for the partial crystallographic information retained within the
data. Here, we proposed an intelligent L12-ordered structure recognition method
based on convolutional neural networks (CNNs). The SDMs of a simulated
L12-ordered structure and the FCC matrix were firstly generated. These
simulated images combined with a small amount of experimental data were used to
train a CNN-based L12-ordered structure recognition model. Finally, the
approach was successfully applied to reveal the 3D distribution of L12-type
$\delta^\prime$-Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a
FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down
to 5 \r{A}. The proposed CNN-APT method is promising to be extended to
recognize other nanoscale ordered structures and even more-challenging
short-range ordered phenomena in the near future.
