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detector”. Conversely, it allows the selection of a region of interest in the image to produce a Bragg Vector
Map (BVM), which collapses the diffraction information into a single image [100,101] . This information can be
collected at various points in the image for further analysis, such as orientation mapping and strain
mapping. Furthermore, py4DSTEM has built-in functionalities that enable users to locate diffraction disks
and make quantitative measurements for parameters such as strain and polarization. This capability ensures
that measurements can be standardized and repeated across a variety of datasets, facilitating repeatable
analysis.

Although programs, such as py4DSTEM, provide a strong foundation for analyzing 4D-STEM datasets,
there are cases where researchers need to develop custom tools for their specific applications. Automated
analysis is the most practical approach for such analyses, but often, custom solutions need to be built. For
instance, some datasets may contain noisy and complex features that require filtering and fitting algorithms.
One recently introduced program is AutoDisk, a Python-based code that performs automated diffraction
processing for strain mapping. Variations in diffraction patterns can arise due to various factors, including
thickness gradients and low probe currents for beam-sensitive materials, which can complicate automated
analysis. AutoDisk addresses these variations by utilizing cross-correlations, blob detection, edge
refinements, and lattice fitting to identify diffraction disks . Once identified, this diffraction information
[102]
can be used for various analyses, including characterizing phase, symmetry, and orientation. While there are
many ways to analyze data, unique solutions may be necessary for analyzing specific datasets. There are
numerous code repositories available for 4D-STEM data analysis, including py4DSTEM, HyperSpy, pyXem,
[19]
LiberTEM, and Pycroscopy, which can serve as a basis for custom analysis .

SUMMARY AND OUTLOOK
In conclusion, electron microscopy is a dynamic and continuously advancing technique with immense
potential for the analysis of ferroic and other functional materials. Ferroic materials exhibit a wide range of
unique properties that can be utilized in countless applications. These properties stem from chemical and
structural inhomogeneities that occur at the atomic scale.

S/TEM offers a distinct advantage in probing these features in both real space and reciprocal space through
electron diffraction measurements. Recent advancements in electron microscopy technology have improved
the usability and enabled unprecedented resolution of these instruments. To fully harness the potential of S/
TEM in the development of advanced materials, it is crucial to make data collection and analysis widely
accessible to researchers. This accessibility will foster further exploration and utilization of S/TEM in
material characterization. The field holds incredible potential, which can be further realized through
ongoing advancements and the collaboration of researchers from various disciplines.

(1) Instrumentation availability and data analysis tools: STEM and TEM play a crucial role in the study of
piezoelectric and other functional materials. While state-of-the-art instruments are not immediately
available to all researchers, instruments are often accessible to external researchers at universities, national
laboratories, and in industry. There are two aspects to make TEM and STEM available to more researchers:

a. Data collection: Modern S/TEM user interfaces are equipped with programmatic capabilities, enabling
users to develop workflows to streamline data collection. S/TEMs can readily interface with Python code or
support the user of custom scripts such as Gatan’s Digital Micrograph. It is essential to promote the open-
source nature of these programs so they can be utilized by researchers from various backgrounds.
Simplifying data collection will allow researchers to allocate more time for analysis and characterization.