Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, we explore the effects of descriptors and hyperparameters on the capability of unsupervised ML methods to distill local structural information, exemplified by discovery of polarization and lattice distortion in Sm doped BiFeO3 (BFO) thin films. We demonstrate that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards were designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows us to discover local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. We further extend the reward driven workflows to disentangle structural factors of variation via optimized variational autoencoder (VAE). Finally, the importance of well-defined rewards was explored as a quantifiable measure of success of the workflow.

翻译：像差校正电子显微镜的快速发展迫切需要建立稳健的方法，用于从成像数据中识别物相、铁电变体以及其他相关的材料结构特征。尽管无监督的聚类与分类方法已广泛用于这些任务，但其性能对分析流程中超参数的选择较为敏感。在本研究中，我们探讨了描述符与超参数对无监督机器学习方法提取局部结构信息能力的影响，并以Sm掺杂BiFeO3（BFO）薄膜中极化与晶格畸变的发现为例进行说明。我们证明，可以采用奖励驱动的方法来优化整个工作流程中的这些关键超参数，其中奖励的设计旨在反映畴壁的连续性与平直度，从而确保分析与材料的物理行为保持一致。该方法使我们能够发现与特定物理行为最匹配的局部描述符，从而深入理解材料的基础物理特性。我们进一步将奖励驱动的工作流程扩展到通过优化的变分自编码器（VAE）来解耦结构变化的因素。最后，我们探讨了明确定义的奖励作为工作流程成功可量化度量的重要性。