The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyzed by human operators, an intrinsically challenging process due to the multiple possible analysis steps and the corresponding need to build and optimize complex analysis workflows. We present a methodology based on the concept of a Reward Function coupled with Bayesian Optimization, to optimize image analysis workflows dynamically. The Reward Function is engineered to closely align with the experimental objectives and broader context and is quantifiable upon completion of the analysis. Here, cross-section, high-angle annular dark field (HAADF) images of ion-irradiated $(Y, Dy)Ba_2Cu_3O_{7-\delta}$ thin-films were used as a model system. The reward functions were formed based on the expected materials density and atomic spacings and used to drive multi-objective optimization of the classical Laplacian-of-Gaussian (LoG) method. These results can be benchmarked against the DCNN segmentation. This optimized LoG* compares favorably against DCNN in the presence of the additional noise. We further extend the reward function approach towards the identification of partially-disordered regions, creating a physics-driven reward function and action space of high-dimensional clustering. We pose that with correct definition, the reward function approach allows real-time optimization of complex analysis workflows at much higher speeds and lower computational costs than classical DCNN-based inference, ensuring the attainment of results that are both precise and aligned with the human-defined objectives.

翻译：电子显微镜的兴起扩展了我们对复杂材料进行纳米级和原子级分辨率成像的能力。由此产生的大量数据集通常由人类操作员分析，但由于存在多种可能的分析步骤以及相应的构建和优化复杂分析工作流程的需求，这本质上是一个具有挑战性的过程。我们提出了一种基于奖励函数概念并结合贝叶斯优化的方法，以动态优化图像分析工作流程。该奖励函数经过精心设计，与实验目标和更广泛的背景紧密对齐，并在分析完成后可量化。在此，以离子辐照的$(Y, Dy)Ba_2Cu_3O_{7-\delta}$薄膜的横截面、高角度环形暗场(HAADF)图像作为模型系统。奖励函数基于预期的材料密度和原子间距形成，并用于驱动经典拉普拉斯-高斯(LoG)方法的多目标优化。这些结果可与深度卷积神经网络(DCNN)分割进行基准比较。在存在额外噪声的情况下，优化后的LoG*方法表现优于DCNN。我们进一步将奖励函数方法扩展到部分无序区域的识别，创建了物理驱动的奖励函数和高维聚类的动作空间。我们提出，在正确定义的情况下，奖励函数方法能够以比经典基于DCNN的推理更快的速度和更低的计算成本，实时优化复杂的分析工作流程，从而确保获得既精确又符合人类定义目标的结果。