发表机构
Department of Materials Science and Engineering, Stanford University; SLAC National Accelerator Laboratory; National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory（材料科学与工程系，斯坦福大学; SLAC国家加速器实验室; 电子显微镜国家中心、分子发现所、伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用机器学习模型从合成数据中检测聚合物衍射峰及其强度，相比传统相关峰检测算法，该模型速度更快且性能更优，为4DSTEM实验近实时可视化开辟可能，有助于理解有机混合离子电子导体结构与性能关系。
AI 中文摘要
有机混合离子电子导体（OMIECs）是一类很有前途的聚合物材料，可用于从神经形态计算到节能电子学和生物电子学等各种应用。尽管其具有高度可调性，但结构特征与诸如电荷载流子迁移率等关键性能特性之间的关系却知之甚少。透射电子显微镜（TEM）中的扫描纳米衍射是阐明这种结构-性能关系的有力探针，但会产生大量嘈杂的数据集，难以解释，因为聚合物反射呈现出几种不同的形态。为了解决这种复杂性，我们训练了一个机器学习（ML）模型，从合成数据中检测这些聚合物衍射峰及其强度。与分析纳米束4D扫描透射电子显微镜（4DSTEM）数据的传统相关峰检测算法相比，我们表明ML模型速度明显更快，并且在几乎所有情况下都优于相关算法，为4DSTEM实验的近实时可视化开辟了可能性。
英文摘要
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.
CommentsSubmitted to ACS Macromolecules. Main text is 13 pages and 5 figures, 23 pages and 15 figures with supporting information
