标题： 基于卷积神经网络的4DSTEM衍射图预测EDS图谱 显示英文标题

标题： Prediction of EDS Maps from 4DSTEM Diffraction Patterns Using Convolutional Neural Networks

摘要： 理解原子结构（秩序）与化学组成（化学）之间的关系对于推进材料科学至关重要，然而传统的光谱技术可能速度慢并对敏感样品造成损害。四维扫描透射电子显微镜（4D-STEM）在扫描区域中捕获详细的衍射图样，提供丰富的结构信息，而能量色散X射线光谱（EDS）提供互补的化学数据。在本工作中，我们开发了一个机器学习框架，可以直接从4D-STEM衍射图样预测EDS光谱，从而减少束流照射和采集时间。卷积神经网络（CNN）准确推断元素组成，特别是对于具有强衍射对比度或较高浓度的元素，如氧和碲。外推和内插策略均表现出一致的性能，当有额外的结构上下文时，预测效果得到改善。视觉和互相关分析证实了模型捕捉全局和局部组成趋势的能力。这种方法建立了一种数据驱动的非破坏性、高通量材料表征途径。 显示英文摘要

摘要： Understanding the relationship between atomic structure (order) and chemical composition (chemistry) is critical for advancing materials science, yet traditional spectroscopic techniques can be slow and damaging to sensitive samples. Four-dimensional scanning transmission electron microscopy (4D-STEM) captures detailed diffraction patterns across scanned regions, providing rich structural information, while energy dispersive X-ray spectroscopy (EDS) offers complementary chemical data. In this work, we develop a machine learning framework that predicts EDS spectra directly from 4D-STEM diffraction patterns, reducing beam exposure and acquisition time. A convolutional neural network (CNN) accurately infers elemental compositions, particularly for elements with strong diffraction contrast or higher concentrations, such as Oxygen and Tellurium. Both extrapolation and interpolation strategies demonstrate consistent performance, with improved predictions when additional structural context is available. Visual and cross-correlation analyses confirm the model's ability to capture global and local compositional trends. This approach establishes a data-driven pathway to non-destructive, high-throughput materials characterization.