Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer, Jesse Farebrother, Joshua Greaves, Kevin Roccapriore, Ekin Cubuk, Rishabh Agarwal, Aaron Courville, Marc Bellemare, Sergei Kalinin, Igor Mordatch, Pablo Castro

Keywords: machine learning, microscopy, material, graphene, silicon, scanning transmission electron microscope

TL;DR: 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).

Abstract: 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.

Submission Track: Papers

Submission Category: AI-Guided Design

Submission Number: 29