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

arXiv: https://arxiv.org/abs/2311.17894 · HTML: https://mlsanity.com/paper/2311.17894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 45 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)

## Abstract (arXiv)

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.