用机器学习解析聚合物电子衍射图，加速材料结构分析
Mapping Order in Semicrystalline Polymers using Machine Learning of Nanobeam Electron Diffraction
- 用合成数据训练模型自动识别纳米束电子衍射中的聚合物衍射峰
- 相比传统方法速度更快，且在绝大多数情况下精度更高
- 适合从事聚合物材料表征与电子显微镜分析的研究者
有机混合离子-电子导体（OMIECs）是一类极具前景的聚合物材料，适用于神经形态计算、低功耗电子学及生物电子学。尽管其性能高度可调，但结构特征与电荷载流子迁移率等关键性能之间的关系仍不清晰。透射电镜中的扫描纳米衍射技术是揭示这种结构-性能关联的强大工具，但会产生大量噪声数据，因聚合物衍射峰呈现多种不同形貌而难以解析。为应对复杂性，我们基于合成数据训练了一种机器学习模型，用于检测聚合物衍射峰及其强度。相比传统的相关峰值检测算法——分析纳米束4D扫描透射电镜（4DSTEM）数据的常规方法——该机器学习模型显著更快速，并在几乎所有情况下表现更优，从而实现4DSTEM实验的近实时可视化。
原文摘要 · Abstract (English)
Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics. Despite being highly tunable, the relationship between structural features and key performance properties such as charge carrier mobility is poorly understood. Scanning nanodiffraction in the transmission electron microscope (TEM) is a powerful probe for elucidating this structure-property relationship, but produces large, noisy datasets that are difficult to interpret because polymer reflections exhibit several distinct morphologies. To address the complexity, we trained a machine learning (ML) model to detect these polymer diffraction peaks and their intensities from synthetic data. Compared to correlative peak detection algorithms, the conventional method for analyzing nanobeam 4D scanning transmission electron microscopy (4DSTEM) data, we show that the ML model is significantly faster and outperforms correlative algorithms in almost all cases, opening up the possibility of near-live visualization of 4DSTEM experiments.
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