Towards automating structural discovery in scanning transmission electron microscopy* This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE- AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Creange, Nicole; Dyck, Ondrej; Vasudevan, Rama K; Ziatdinov, Maxim; Kalinin, Sergei V
• Is Part Of: Machine learning: science and technology. Volume 3:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: STEM -- Bayesian optimization -- automated experiments -- data acquisition workflows; 006.31
• DOI: 10.1088/2632-2153/ac3844
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of 'active learning' methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO3 matrix, ferroelectric domains in BiFeO3, and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .