Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk

Abstract

Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron beam on the atomic level. However, despite the rate at which large volumes of data can be generated (sometimes in the gigabyte to terabyte range per single experiment), approaches for the extraction of material-specific knowledge on the kinetics and thermodynamics of these processes are still lacking. One of the critical issues lies in being able to map the evolution of various atomic structures and determine the associated transition probabilities directly from raw experimental data characterized by high levels of noise and missing structural elements. Here, we demonstrate an approach based on the combination of multiple machine learning techniques to study the dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene in STEM experiments. First, a deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. Then, 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. Finally, the frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the thermodynamics of defect populations and chemical reaction networks from the atomically resolved STEM data. Here, we observe a clear tendency 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.

References in corpus (9)

• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images
• Manipulating low-dimensional materials down to the level of single atoms with electron irradiation
• Imaging Atomic-Level Random Walk of a Point Defect in Graphene
• Electron-Beam Manipulation of Silicon Dopants in Graphene
• Assembling Di- and Multiatomic Si Clusters in Graphene via Electron Beam Manipulation
• Towards atomically precise manipulation of 2D nanostructures in the electron microscope
• Direct atomic fabrication and dopant positioning in Si using electron beams with active real time image-based feedback
• E-beam manipulation of Si atoms on graphene edges with aberration-corrected STEM

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• Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties
• Doping of Cr in Graphene Using Electron Beam Manipulation for Functional Defect Engineering
• Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data
• Revealing the chemical bonding in adatoms arrays via machine learning of 3D scanning tunneling spectroscopy data
• Identification and Correction of Temporal and Spatial Distortions in Scanning Transmission Electron Microscopy
• Unsupervised learning of ferroic variants from atomically resolved STEM images
• Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline