  Scanning Transmission Electron Microscopy (STEM) coupled with Electron Energy
Loss Spectroscopy (EELS) presents a powerful platform for detailed material
characterization via rich imaging and spectroscopic data. Modern electron
microscopes can access multiple length scales and sampling rates far beyond
human perception and reaction time. Recent advancements in machine learning
(ML) offer a promising avenue to enhance these capabilities by integrating ML
algorithms into the STEM-EELS framework, fostering an environment of active
learning. This work enables the seamless integration of STEM with
High-Performance Computing (HPC) systems. We present several implemented
workflows that exemplify this integration. These workflows include
sophisticated techniques such as object finding and Deep Kernel Learning (DKL).
Through these developments, we demonstrate how the fusion of STEM-EELS with ML
and HPC enhances the efficiency and scope of material characterization for 70%
STEM available globally. The codes are available at GitHub link.
