stemOrchestrator: Enabling Seamless Hardware Control and High- Throughput Workflows on Electron Microscopes.

Scanning Transmission Electron Microscopy (STEM) is one of the most powerful tools for materials characterization, providing access to atomic-scale structure via direct imaging, chemical composition via spectral methods, and crystallographic information through diffraction. However, these diverse functionalities are often supported by different hardware components from different manufacturers, creating challenges in seamless operation and integration. As the field moves toward machine learning (ML)-enabled experiments and autonomous discovery, the need for coordinated control across these systems becomes critical. Traditional setups lack cohesive automation solutions capable of managing multiple hardware elements and executing complex, adaptive workflows. This paper presents stemOrchestrator, a software framework designed to overcome these obstacles by offering a cohesive platform for controlling various STEM hardware modules and developing sophisticated automated workflows. The functionality of stemOrchestrator, is demonstrated through its ability to efficiently control multiple hardware components, such as beams, stages, and detectors, and execute intricate workflows like Particle-mapping with increased precision and efficiency. Additionally, drift correction integration ensures the reliability of long-term experiments. This research lays the groundwork for a new era in STEM automation, facilitating rapid, reproducible, and collaborative studies in materials characterization. This framework also enables LLM(Large language model) agents to potentially intervene, suggest and run complex automated workflows. The codes are available at this link for trying and contributing: https://github.com/pycroscopy/pyAutoMic/tree/main/TEM/stemOrchestrator