A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data.

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of segregation and microstructure in modern multi-component materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multi-stage machine learning strategy to identify compositionally distinct domains in a semi-automated way, and subsequently quantify their geometric and compositional characteristics.

Simulation of the θ' Precipitation Process with Interfacial Anisotropy Effects in Al-Cu Alloys.

The effects of anisotropic interfacial properties and heterogeneous elasticity on the growth and ripening of plate-like θ'-phase (AlCu) in Al-1.69 at.% Cu alloy are studied.