Lu Shizhao, Jayaraman Arthi
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.
Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States.
JACS Au. 2023 Jul 21;3(9):2510-2521. doi: 10.1021/jacsau.3c00275. eCollection 2023 Sep 25.
In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability 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 technique(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 (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from Doerk G. S.; et al. Sci. Adv.2023, 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 material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.
在材料研究中，结构表征通常需要多种互补技术来全面了解合成材料的形态。根据不同表征技术（如散射、显微镜、光谱学）的可用性和可及性，每个研究机构或学术研究实验室可能在一种技术上具备高通量能力，但在其他技术方面面临限制（样品制备、分辨率、获取时间）。此外，一种类型的结构表征数据可能比另一种更容易解释（例如，显微镜图像比小角散射曲线更容易解释）。因此，拥有能够基于来自多种技术的配对结构表征数据（易于解释和难以解释、数据收集或样品制备速度快和慢）进行训练的机器学习模型是很有用的，这样模型就可以从一种数据生成另一组表征数据。在本文中，我们展示了一种这样的机器学习工作流程，即配对变分自编码器（PairVAE），它适用于来自小角X射线散射（SAXS）的数据，这些数据呈现有关整体形态的信息，以及来自扫描电子显微镜（SEM）的图像，这些图像呈现样品的二维局部结构信息。使用新观察到的嵌段共聚物组装形态的配对SAXS和SEM数据[来自Doerk G. S.等人的开放获取数据。《科学进展》2023年，9（（2），），eadd3687]，我们训练了我们的PairVAE。成功训练后，我们证明当PairVAE将样品的相应SAXS二维图案作为输入时，它可以生成嵌段共聚物形态的SEM图像，反之亦然。该方法也可以扩展到其他软材料形态，并作为一种有价值的工具，用于轻松解释二维SAXS图案，以及作为生成类似显微镜图像集合的引擎，以创建用于其他下游结构-性能关系计算的数据库。
