Unsupervised machine learning applied to scanning precession electron diffraction data

Duncan N. Johnstone,Antonius T. J. van Helvoort,Paul A. Midgley,Ben H. Martineau,Alexander S. Eggeman

doi:10.1186/s40679-019-0063-3

Abstract

Scanning precession electron diffraction involves the acquisition of a two-dimensional precession electron diffraction pattern at every probe position in a two-dimensional scan. The data typically comprise many more diffraction patterns than the number of distinct microstructural volume elements (e.g. crystals) in the region sampled. A dimensionality reduction, ideally to one representative diffraction pattern per distinct element, may then be sought. Further, some diffraction patterns will contain contributions from multiple crystals sampled along the beam path, which may be unmixed by harnessing this oversampling. Here, we report on the application of unsupervised machine learning methods to achieve both dimensionality reduction and signal unmixing. Potential artefacts are discussed and precession electron diffraction is demonstrated to improve results by reducing the impact of bending and dynamical diffraction so that the data better approximate the case in which each crystal yields a given diffraction pattern.

Highlights

Scanning transmission electron microscopy (STEM) investigations increasingly combine the measurement of multiple analytical signals as a function of probe position with post-facto computational analysis [1]
We surmise that precession leads to the data better approximating the situation where there is a single diffraction pattern associated with each microstructural element, which here is essentially the two twinned crystal orientations and the vacuum surrounding the sample
Our results indicate that either machine learning method is superior to conventional linear decomposition for the analysis of studies have primarilyScanning precession electron diffraction (SPED) datasets, but some unintuitive and potentially misleading features are present in the learning results

Summary

Introduction

Scanning transmission electron microscopy (STEM) investigations increasingly combine the measurement of multiple analytical signals as a function of probe position with post-facto computational analysis [1]. In analytical electron microscopy, such methods have been applied to learn representative signals corresponding to separate microstructural elements (e.g. crystal phases) and to unmix signals comprising contributions from multiple microstructural elements sampled along the beam path [3,4,5,6,7,8,9,10]. VBF/VDF analysis has been used to provide insight into

Methods

Results

Discussion

Conclusion