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.
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