Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

• URL: http://arxiv.org/abs/2311.17894v1
• Date: Tue, 21 Nov 2023 21:51:00 GMT
• Title: Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
• 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 summary: We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms. 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.
• Score: 58.51812955462815
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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