Machine learning methods are progressively gaining acceptance in the electron
microscopy community for de-noising, semantic segmentation, and dimensionality
reduction of data post-acquisition. The introduction of the APIs by major
instrument manufacturers now allows the deployment of ML workflows in
microscopes, not only for data analytics but also for real-time decision-making
and feedback for microscope operation. However, the number of use cases for
real-time ML remains remarkably small. Here, we discuss some considerations in
designing ML-based active experiments and pose that the likely strategy for the
next several years will be human-in-the-loop automated experiments (hAE). In
this paradigm, the ML learning agent directly controls beam position and image
and spectroscopy acquisition functions, and human operator monitors experiment
progression in real- and feature space of the system and tunes the policies of
the ML agent to steer the experiment towards specific objectives.

机器学习在电子显微镜领域逐步获得认可，用于数据后处理中的去噪、语义分割和降维。在使用大型仪器制造商的 API 引入后，现在可以在显微镜中部署机器学习工作流，不仅用于数据分析，还用于实时决策和反馈。然而，实时机器学习的用例数量仍然相当有限。在本文中，我们讨论了设计基于机器学习的主动实验的一些考虑因素，并提出了未来几年的策略可能是人在环回中的自动化实验 (hAE)。在这种范例中，机器学习代理直接控制光束位置、图像和光谱采集功能，人操作员实时监控系统的特征空间中的实验进展，并调整机器学习代理的策略以实现特定目标。