Towards Automating Structural Discovery in Scanning Transmission Electron Microscopy

TL;DR

This paper develops and compares automated workflows using active learning and deep learning techniques to improve structural discovery in scanning transmission electron microscopy, addressing the challenge of selecting relevant regions efficiently.

Contribution

It introduces multiple automated experiment workflows that incorporate Bayesian optimization, deep learning, and variational autoencoders to enhance structural discovery in STEM imaging.

Findings

Demonstrates tradeoffs between accuracy and robustness of different workflows.

Shows effectiveness of deep learning descriptors and autoencoders in identifying features.

Provides open-source code for reproducibility and further research.

Abstract

Scanning transmission electron microscopy (STEM) 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:SrMnO$_3$ matrix, ferroelectric domains in BiFeO$_3$, and topological defects in graphene. The code developed in this manuscript are open sourced and will be released at github.com/creangnc/AE_Workflows.