The construction of a deep learning model and visualization of judgment regions were conducted for X-ray diffraction and scattering images of aliphatic polyesters. Due to recent progress in measurement methods, a large amount of image data can be obtained in a short time; therefore, machine learning methods are useful to determine the important regions for a given objective. Although techniques to visualize the judgment regions using deep learning have recently been developed, there have been few reports discussing whether such models can determine the important regions of X-ray diffraction and scattering images of polymeric materials. Herein, we demonstrate classification models based on convolutional neural networks (CNNs) for wide-angle X-ray diffraction and small-angle X-ray scattering images of aliphatic polyesters to predict the types of polymers and several crystallization temperatures. Furthermore, the judgment regions of the X-ray images used by the CNNs were visualized using the Grad-CAM, LIME, and SHAP methods. The main regions were diffraction and scattering peaks recognized by experts. Other areas, such as the beam centers were recognized when the intensity of the images was randomly changed. This result may contribute to developing important features in deep learning models, such as the recognition of structure–property relationships.

对脂肪族聚酯的X射线衍射和散射图像进行了深度学习模型的构建和判断区域的可视化。由于最近测量方法的进步，可以在短时间内获得大量的图像数据；因此，机器学习方法可用于确定给定目标的重要区域。尽管最近开发了使用深度学习来可视化判断区域的技术，但很少有报告讨论此类模型是否可以确定聚合物材料的 X 射线衍射和散射图像的重要区域。在此处，我们展示了基于卷积神经网络 (CNN) 的分类模型，用于脂肪族聚酯的广角 X 射线衍射和小角 X 射线散射图像，以预测聚合物的类型和几种结晶温度。此外，CNN 使用的 X 射线图像的判断区域使用 Grad-CAM、LIME 和 SHAP 方法进行可视化。主要区域是专家认可的衍射和散射峰。当图像的强度随机改变时，可以识别其他区域，例如光束中心。这一结果可能有助于开发深度学习模型中的重要特征，例如结构-性质关系的识别。CNN 使用的 X 射线图像的判断区域使用 Grad-CAM、LIME 和 SHAP 方法进行可视化。主要区域是专家认可的衍射和散射峰。当图像的强度随机改变时，可以识别其他区域，例如光束中心。这一结果可能有助于开发深度学习模型中的重要特征，例如结构-性质关系的识别。CNN 使用的 X 射线图像的判断区域使用 Grad-CAM、LIME 和 SHAP 方法进行可视化。主要区域是专家认可的衍射和散射峰。当图像的强度随机改变时，可以识别其他区域，例如光束中心。这一结果可能有助于开发深度学习模型中的重要特征，例如结构-性质关系的识别。