{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-and-controlling-silicon-dopant","title":"Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy","arxiv_id":"2311.17894","date":"2023-11-21","proceeding":null,"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"],"abstract":"We introduce a machine learning approach 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). 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 probabilities. These learned transition dynamics are then leveraged 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.","url_abs":"https://arxiv.org/abs/2311.17894v1","url_pdf":"https://arxiv.org/pdf/2311.17894v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-and-controlling-silicon-dopant","repo_url":"https://github.com/google/putting-dune","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}