In material research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of the synthesized material. Depending on the availability of and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other techniques(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, PairVAE, that works with data from Small Angle X-Ray Scattering (SAXS) that presents information about bulk morphology and images from Scanning Electron Microscopy (SEM) that presents two-dimensional local structural information of the sample. Using paired SAXS and SEM data of novel block copolymer assembled morphologies [open access data from Doerk G.S., et al. Science Advances. 2023 Jan 13;9(2): eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern, and vice versa. This method can be extended to other soft materials morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as creating a database for other downstream calculations of structure-property relationships.

翻译：在材料研究中，结构表征通常需要多种互补技术来获得合成材料的整体形貌视图。根据不同表征技术（例如散射、显微术、光谱学）的可用性和可及性，各研究机构或学术实验室可能在某项技术上具备高通量能力，但在其他技术上受限于（样品制备、分辨率、使用时间）等因素。此外，一种结构表征数据可能比另一种更易解读（例如，显微图像比小角散射轮廓更易解读）。因此，若能基于多种技术的配对结构表征数据训练机器学习模型，使模型能从一类表征数据生成另一类数据，将具有重要实用价值。本文展示了这样一种机器学习工作流程——PairVAE，该模型可处理从小角X射线散射（SAXS）获得的体相形貌信息数据，以及从扫描电子显微镜（SEM）获得的样品二维局部结构图像数据。我们利用新型嵌段共聚物组装形貌的配对SAXS与SEM数据（公开数据来自Doerk G.S.等人，《科学进展》2023年1月13日；9(2)：eadd3687）训练PairVAE。成功训练后，我们证明PairVAE在输入样品对应的SAXS二维图案时能生成该样品嵌段共聚物形貌的SEM图像，反之亦然。该方法还可拓展至其他软物质形貌，为二维SAXS图案的便捷解读及构建结构-性能关系下游计算数据库提供宝贵工具。