Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy.
Kevin M RoccaprioreMatthew G BoebingerOndrej DyckAyana GhoshRaymond R UnocicSergei V KalininMaxim ZiatdinovPublished in: ACS nano (2022)
A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.
Keyphrases
- electron microscopy
- convolutional neural network
- deep learning
- transition metal
- machine learning
- electronic health record
- high throughput
- big data
- molecular dynamics
- single molecule
- molecular dynamics simulations
- magnetic resonance
- radiation therapy
- high resolution
- computed tomography
- ionic liquid
- neural network
- data analysis