Machine Learning for Atom Probe Tomography?

Atom probe tomography (APT) is an atomic scale materials characterisation technique. Utilising high-field emission, APT works by applying an electric field (between 10-60 V/nm), assisted by the application of a pulsed-laser for semi- and non-conductive samples, to a small needle shaped specimen. Ionised atoms at the sample tip are propelled through the electric field towards a multi-channel plate detector, where time-of-flight and x- and y-coordinates are recorded. Z-coordinates are calculated post-experimentation during the reconstruction process and are based on the sequence of events recorded at the detector. The end product of this process is an atomic 3D reconstruction of the sample from which valuable information as to the distribution of minor constituents within the sample, the grain boundary chemistry and more can be obtained. As the assortment of APT samples expands to include heterogeneous materials with complex field behaviour, the application of traditional reconstruction methods is no longer sufficient to produce highly accurate representations of the original samples. As such, the APT community is currently searching out new and novel methods of handling increasingly complex atom probe datasets.