Speaker
Description
Atom probe tomography (APT) is a materials analysis technique that provides sub-nanometer resolution compositional mapping. The data is in the form of a point cloud containing often millions of atoms, and to each of these points is assocaited an elemental nature. By interrogating the point cloud, the local composition of a material or a phase of a specific microstructural feature can be reported. APT is often referred to as "data-intensive" technique, and has long made use of many clustering-type techniques (DBSCAN, NN etc.) to facilitate data extraction, which are all now often classified as belonging to machine-learning.
In this presentation, I will review some of the recent developments from MPIE in the application of machine-learning techniques to atom probe analysis workflows – i.e. beyond just extraction of data from the point cloud – targeting faster and more efficient, reliable and reproducible data analysis.
