Learning and Controlling Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy
Abstract A machine learning approach is introduced to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). This method is data‐centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which is used to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre‐determined target destinations. Empirical analyses are presented that demonstrate the efficacy and generality of the approach.
Authors
- Marc G. Bellemare (ORCID: https://orcid.org/0000-0002-6096-0105)
- Igor Mordatch
- Max Schwarzer
- Kevin M. Roccapriore (ORCID: https://orcid.org/0000-0001-9907-6790)
- Sergei V. Kalinin (ORCID: https://orcid.org/0000-0001-5354-6152)
- Ekin D. Cubuk (ORCID: https://orcid.org/0000-0003-0524-2837)
- Pablo Samuel Castro (ORCID: https://orcid.org/0000-0002-3206-336X)
- Rishabh Agarwal (ORCID: https://orcid.org/0000-0003-2817-0231)
- Joshua Greaves
- Jesse Farebrother
- Aaron Courville (ORCID: https://orcid.org/0000-0001-6223-0301)
Institutions
- Oak Ridge National Laboratory (US)
- Google (United States) (US)
- McGill University (CA)
- Université de Montréal (CA)
- University of Tennessee at Knoxville (US)
Publication Details
- Journal
- Advanced Materials Interfaces
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1002/admi.202400998
- Primary Topic
- Advanced Electron Microscopy Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- DeepMind
- Office of Science
- Basic Energy Sciences
- Oak Ridge National Laboratory
- Energy Frontier Research Centers