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

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

doi:10.1038/s41524-021-00613-6

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

Journal Article · 16 September 2021 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-021-00613-6 · OSTI ID:1831683

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^[1]; Takeuchi, Ichiro ^[2];

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Maryland, College Park, MD (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Institutes of Standards and Technology (NIST)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1831683

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 7; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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