Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Abstract

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.

Authors

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

Venue

NeurIPS Workshop 2023