原子分辨显微镜中的学习模体及其层次结构
Learning Motifs and their Hierarchies in Atomic Resolution Microscopy
摘要：
高通量材料合成的进展和高通量计算的发展加速了功能材料发现的进展。然而，一个互补的强大的和高通量的结构表征框架仍然缺乏。机器学习领域的新方法和工具表明，基于原子级成像的高度自动化的高通量结构表征框架可以建立结构和宏观性质之间的关键统计联系。在这里，我们开发了一个机器学习框架来实现这一目标。我们的框架捕捉局部结构特征的图像与泽尼克多项式，这是证明噪声鲁棒性，灵活性和准确性。这些功能，然后被分类到易于解释的结构图案与分层主动学习计划提供动力的一种新的无监督的两阶段放松聚类方案。我们已经成功地证明了所提出的方法的准确性和效率，通过映射全谱的结构缺陷，包括点缺陷，线缺陷，和平面缺陷的扫描透射电子显微镜（STEM）图像的各种二维材料，大大提高了分离比现有的方法。我们的技术可以轻松灵活地应用于其他类型的显微镜数据与复杂的功能，提供了一个坚实的基础，自动，多尺度的特征分析，具有高准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
