On the use of advanced scanning transmission electron microscopy and machine learning for studying multi-component materials

Abstract

The nanoscale distribution of elements in two multi-component materials is assessed by unsupervised machine learning methods. These are compared to elemental maps to highlight the potential shortcomings of simplistic compositional analyses. Quantification of the resulting microstructure components provides insight into the evolution of the microstructure and the possible reasons for the misinterpretation of the element maps.