We introduce a machine learning approach to determining the transition rates of silicon atoms on graphene (a lattice of carbon atoms), when stimulated by the electron beam of a scanning transmission electron microscope. Our learned rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present sensitivity and accuracy analyses that demonstrate the generality of our approach.