Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
This work addresses the challenge of precise atomic manipulation in materials science, representing an incremental advancement in applying machine learning to nanoscale control.
The paper tackled the problem of controlling silicon atom transitions in graphene using a scanning transmission electron microscope by introducing a machine learning approach to predict transition probabilities and guide atoms to target destinations, with empirical analyses demonstrating efficacy and generality.
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.