Robust design of semi-automated clustering models for 4D-STEM datasets

Alexandra Bruefach, Colin Ophus, Mary Scott

Keywords: Materials Discovery, Materials Characterization, Diffraction, 4D-STEM, Unsupervised Learning, Consensus Clustering

TL;DR: Detection of unique structural regions in novel materials can be accomplished by performing Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) and using consensus clustering on engineered feature representations.

Abstract: Materials discovery and design require characterizing material structure at the nanometer and sub-nanometer scale. Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) resolves the crystal structure of materials, but many 4D-STEM data analysis pipelines are not suited for identification of anomalous and unexpected structures. This work introduces improvements to the iterative Non-Negative Matrix Factorization (NMF) method by implementing consensus clustering for ensemble learning. We evaluate the performance of models during parameter tuning and find that consensus clustering improves performance in all cases and is able to recover specific grains missed by the best performing model in the ensemble. The methods introduced in this work can be applied broadly to materials characterization datasets to aid in the design of new materials.

Paper Track: Papers

Submission Category: Automated Material Characterization