Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

Christopher T Nelson,Xiaohang Zhang,Maxim Ziatdinov,Ichiro Takeuchi,Mark Oxley,Ayana Ghosh,Sergei V Kalinin

doi:10.1038/s41524-021-00613-6

Abstract

Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

Highlights

The functionality of ferroelectric materials is inseparably linked to the static distributions and dynamic behaviors of the polarization[1–5]
As a model system we explore a thin film of the Sm-doped ferroelectric BiFeO3 (BFO) epitaxially grown on a SrTiO3 (STO) substrate as a combinatorial library with Sm concentration varying from 0 to 20%
For BFO the ferroelectric polarization strongly couples with the lattice, notably the heavy cation Bi and Fe sublattices which are readily imaged by atomic-resolution scanning transmission electron microscopy (STEM), and this cation non-centrosymmetry is used as a proxy for the ferroelectric polarization vector

Summary

INTRODUCTION

The functionality of ferroelectric materials is inseparably linked to the static distributions and dynamic behaviors of the polarization[1–5]. Since the early work of Miller and Weinreich[25] and Burtsev and Chervonobrodov[26–28] it has been realized that domain wall motion proceeds via the generation of kinks in the domain walls This further results in strong interactions between topological defects in ferroelectrics and charged impurities, giving rise to unique functionalities of ferroelectric relaxors[29–31]. Quantitative analysis of STEM data can provide insight regarding the corresponding mechanisms[43,50] This analysis has been extended toward the Bayesian analysis of domain wall structures, allowing incorporation of past knowledge of materials physics into the model and quantifying the requirements to microscopic systems required to identify specific aspects of physical behaviors[51]. We explore the applications of deep convolutional neural networks (DCNNs) for reconstruction and segmentation of STEM images of ferroelectric materials and explore some of the potential sources of observational biases in this analysis

RESULTS AND DISCUSSION

METHODS

CODE AVAILABILITY