  Progress in functional materials discovery has been accelerated by advances
in high throughput materials synthesis and by the development of
high-throughput computation. However, a complementary robust and high
throughput structural characterization framework is still lacking. New methods
and tools in the field of machine learning suggest that a highly automated
high-throughput structural characterization framework based on atomic-level
imaging can establish the crucial statistical link between structure and
macroscopic properties. Here we develop a machine learning framework towards
this goal. Our framework captures local structural features in images with
Zernike polynomials, which is demonstrably noise-robust, flexible, and
accurate. These features are then classified into readily interpretable
structural motifs with a hierarchical active learning scheme powered by a novel
unsupervised two-stage relaxed clustering scheme. We have successfully
demonstrated the accuracy and efficiency of the proposed methodology by mapping
a full spectrum of structural defects, including point defects, line defects,
and planar defects in scanning transmission electron microscopy (STEM) images
of various 2D materials, with greatly improved separability over existing
methods. Our techniques can be easily and flexibly applied to other types of
microscopy data with complex features, providing a solid foundation for
automatic, multiscale feature analysis with high veracity.
