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Directed atomic fabrication using an aberration-corrected scanningtransmission electron microscope (STEM) opens new pathways for atomicengineering of functional materials. In this approach, the electron beam isused to actively alter the atomic structure through electron beam inducedirradiation processes. One of the impediments that has limited widespread usethus far has been the ability to understand the fundamental mechanisms ofatomic transformation pathways at high spatiotemporal resolution. Here, wedevelop a workflow for obtaining and analyzing high-speed spiral scan STEMdata, up to 100 fps, to track the atomic fabrication process during nanoporemilling in monolayer MoS2. An automated feedback-controlled electron beampositioning system combined with deep convolution neural network (DCNN) wasused to decipher fast but low signal-to-noise datasets and classifytime-resolved atom positions and nature of their evolving atomic defectconfigurations. Through this automated decoding, the initial atomic disorderingand reordering processes leading to nanopore formation was able to be studiedacross various timescales. Using these experimental workflows a greater degreeof speed and information can be extracted from small datasets withoutcompromising spatial resolution. This approach can be adapted to other 2Dmaterials systems to gain further insights into the defect formation necessaryto inform future automated fabrication techniques utilizing the STEM electronbeam.

When the atoms dance: exploring mechanisms of electron-beam induced  modifications of materials with machine-learning assisted high temporal  resolution electron microscopy