发表机构
Philipps-Universität Marburg; Philipps-Universität Marburg, Department of Physics（马尔堡菲利普斯大学; 马尔堡菲利普斯大学物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用人工神经网络（包括CNN和ViT）对4DSTEM衍射图样进行训练，以区分LiNiO2和NiO相，在合成与实验数据上均展现出稳健性能，优于传统模板匹配方法。
AI 中文摘要
在材料分析领域，识别不同的材料相至关重要。以神经网络形式出现的人工智能提供了一种非常快速且训练完成后计算成本低廉的方法，用于分析大量图像数据，例如在四维扫描透射电子显微镜（4DSTEM）数据集采集过程中生成的衍射图样集。在本工作中，我们在此类图像上训练多种网络架构，以区分LiNiO2和NiO的相，这是锂离子电池领域中一个重要且具有挑战性的区分任务，因为NiO的形成会限制电池容量。我们测试了经典卷积神经网络（CNNs）和不同形式的视觉变换器（ViTs）。我们的网络在合成图像上训练，并在实验记录的衍射图样上进行测试。此外，我们还使用GradCAM方法研究了网络的决策过程。我们在合成和实验衍射图样上测试了我们的网络。我们的网络表现出非常稳健的结果，尤其是在处理高度变化的数据时，而传统模板匹配方法通常在此类数据上表现不佳。
英文摘要
In the realm of material analysis, identifying different material phases is of key importance. Artificial intelligence in the form of neural networks provides a very fast and, once trained, computationally inexpensive method for analysing large amounts of image data, like the sets of diffraction patterns generated during four-dimensional scanning transmission electron microscopy (4DSTEM) dataset acquisition. In this work, we train multiple network architectures on images of this type to distinguish between the phases of LiNiO2 and NiO, an important and challenging distinction in the lithium-ion battery community, since the formation of NiO limits battery capacity. We test both classical convolutional neural networks (CNNs) and different forms of vision transformers (ViTs). Our networks are trained on synthetic images and tested on experimentally recorded diffraction patterns. Additionally, we also investigate the decision-making of our networks using the GradCAM method. We test our networks on both synthetic as well as experimental diffraction patterns. Our networks exhibit very robust results, especially when dealing with highly varying data, an area where traditional template-matching methods typically struggle.
