Spotlight
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer · Jesse Farebrother · Joshua Greaves · Kevin Roccapriore · Ekin Dogus Cubuk · Rishabh Agarwal · Aaron Courville · Marc Bellemare · Sergei Kalinin · Igor Mordatch · Pablo Samuel Castro
Keywords: [ scanning transmission electron microscope ] [ material ] [ microscopy ] [ machine learning ] [ graphene ] [ silicon ]
We introduce a machine learning approach to determine the transition rates 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 rates. These rates are then applied 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.