@inproceedings{
schwarzer2023learning,
title={Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy},
author={Max Schwarzer and Jesse Farebrother and Joshua Greaves and Kevin Roccapriore and Ekin Cubuk and Rishabh Agarwal and Aaron Courville and Marc Bellemare and Sergei Kalinin and Igor Mordatch and Pablo Castro},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=XtEwUzaXJ3}
}
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.
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