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

Abstract

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 due to multiple Api’s (Application programming interface). As the field moves toward machine learning (ML) enabled experiments and autonomous discovery, the need for combined control across these hardware-api’s becomes critical. This paper develops stemOrchestrator, a software framework which combines all the api’s in a cohesive platform for controlling various STEM hardware modules and developing sophisticated automated workflows. We illustrate its usefulness (however not bound to only these) using three workflows, high-throughput particle characterization, Hardware tuning using Bayesian Optimization (BO)and cross correlation-based drift correction with informative logging of hardware status. 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