Speaker
Description
同步辐射X射线散射技术是研究高分子薄膜材料多尺度结构演变的有力工具。其中，时间分辨原位小角X射线散射实验将产生大量SAXS数据。通过分析SAXS散射花样，研究人员可以获得大量硬弹性iPP薄膜在拉伸过程中的结构演变信息。然而，传统手动数据处理方式的带宽有限，无法适应现代高帧率检测器不断增加的数据密度和数量，这可能会导致大数据中包含的关键信息丢失。因此，迫切需要一种能够对大规模SAXS数据集进行预处理并快速探索参数空间的新方法。在这项工作中，我们应用几种机器学习方法来处理硬弹性iPP膜的原位SAXS数据集。结果表明，VAE和cVAE模型能够提取SAXS花样中隐含的结构信息，并将其映射到低维空间中。SAXS散射花样在二维和一维潜空间中的表示揭示了iPP膜的关键特征，如微观结构相似性、结构变化率和结构演化路径，从而可以为研究人员进一步指出数据分析的方向。为了建立加工-结构关系图谱，我们发展了一种混合VAE和多层感知器（MLP）神经网络，其中MLP用于将加工参数映射到潜空间坐标，VAE解码器用于生成给定加工参数下的iPP薄膜SAXS散射花样。为了以测试模型的鲁棒性，我们进行了验证实验，并在连续温度-应变空间中生成了硬弹性iPP膜的SAXS散射花样，以帮助研究人员更全面地了解结构演变，并指导高目标性实验。这项工作中开发的机器学习方法可以用于大型SAXS数据集的快速预处理，及加工-结构关系的映射。此外，该工作所发展的方法框架是通用的，因此未来可扩展到其他需要高通量数据分析的材料体系。
Summary
With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films.
