The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by morphology type, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were developed to predict morphological features based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances ($R^2$ > 0.75), while AFM-based property predictions were less accurate ($R^2$ < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using Shapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

翻译：嵌段共聚物（BCPs）的形貌对其材料性能和应用具有关键影响。本研究引入了一种基于机器学习（ML）的高通量框架，用于分析掠入射小角X射线散射（GISAXS）数据和原子力显微镜（AFM）图像，以表征BCP薄膜的形貌。我们训练了一个卷积神经网络对AFM图像按形貌类型进行分类，测试准确率达到97%。随后对分类后的图像进行分析，以高通量方式从样品中提取二维晶粒尺寸测量结果。我们开发了机器学习模型，以基于溶剂比例、添加剂类型和添加剂比例等加工参数来预测形貌特征。基于GISAXS的性能预测表现出色（$R^2$ > 0.75），而基于AFM的性能预测准确性较低（$R^2$ < 0.60），这可能是由于AFM测量具有局部性，而GISAXS捕获的是整体信息。除了模型性能外，我们还使用沙普利加性解释（SHAP）方法处理可解释性问题。SHAP分析表明，添加剂比例对形貌预测的影响最大，其中添加剂为BCP链提供了更大的体积，使其能够重排为热力学上更有利的形貌。这种可解释性有助于验证模型预测，并揭示参数的重要性。总之，本文提出的结合高通量表征和可解释机器学习的框架，为在广泛的加工参数空间中探索和优化BCP薄膜形貌提供了一种方法。