Abstract
The dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene is examined using scanning transmission electron microscopy (STEM). A deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. A Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. The frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the defect populations and chemical transformation networks from the atomically resolved STEM data. Here, a clear tendency is observed for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.
| Original language | English |
|---|---|
| Article number | 1904480 |
| Journal | Advanced Functional Materials |
| Volume | 29 |
| Issue number | 52 |
| DOIs | |
| State | Published - Dec 1 2019 |
Funding
This research was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division (STEM work by S.V.K., O.D., S.J.). This research was in part conducted at the Center for Nanophase Materials Sciences (data analysis by M.Z.), which is a DOE Office of Science User Facility.
Keywords
- atom dynamics
- electron microscopy
- graphene
- machine learning