  We introduce a machine learning approach to determine the transition dynamics
of silicon atoms on a single layer of carbon atoms, when stimulated by the
electron beam of a scanning transmission electron microscope (STEM). Our method
is data-centric, leveraging data collected on a STEM. The data samples are
processed and filtered to produce symbolic representations, which we use to
train a neural network to predict transition probabilities. These learned
transition dynamics are then leveraged to guide a single silicon atom
throughout the lattice to pre-determined target destinations. We present
empirical analyses that demonstrate the efficacy and generality of our
approach.
