Learning Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy
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.
Cite
Text
Schwarzer et al. "Learning Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy." NeurIPS 2023 Workshops: AI4Mat, 2023.Markdown
[Schwarzer et al. "Learning Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy." NeurIPS 2023 Workshops: AI4Mat, 2023.](https://mlanthology.org/neuripsw/2023/schwarzer2023neuripsw-learning/)BibTeX
@inproceedings{schwarzer2023neuripsw-learning,
title = {{Learning Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy}},
author = {Schwarzer, Max and Farebrother, Jesse and Greaves, Joshua and Roccapriore, Kevin and Cubuk, Ekin and Agarwal, Rishabh and Courville, Aaron and Bellemare, Marc and Kalinin, Sergei and Mordatch, Igor and Castro, Pablo},
booktitle = {NeurIPS 2023 Workshops: AI4Mat},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/schwarzer2023neuripsw-learning/}
}