Learning and Controlling Silicon Dopant Transitions in Graphene using
  Scanning Transmission Electron Microscopy
Max Schwarzer Jesse Farebrother Joshua Greaves Ekin Dogus Cubuk Rishabh Agarwal Aaron Courville Marc G. Bellemare Sergei Kalinin Igor Mordatch Pablo Samuel Castro Kevin M. Roccapriore
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.