$\text{ab initio}$ description of bonding for transmission electron microscopy

Abstract

The simulation of transmission electron microscopy (TEM) images or diffraction patterns is often required to interpret their contrast and extract specimen features. This is especially true for high-resolution phase-contrast imaging of materials, but electron scattering simulations based on atomistic models are widely used in materials science and structural biology. Since electron scattering is dominated by the nuclear cores, the scattering potential is typically described by the widely applied independent atom model. This approximation is fast and fairly accurate, especially for scanning TEM (STEM) annular dark-field contrast, but it completely neglects valence bonding and its effect on the transmitting electrons. However, an emerging trend in electron microscopy is to use new instrumentation and methods to extract the maximum amount of information from each electron. This is evident in the increasing popularity of techniques such as 4D-STEM combined with ptychography in materials science, and cryogenic microcrystal electron diffraction in structural biology, where subtle differences in the scattering potential may be both measurable and contain additional insights. Thus, there is increasing interest in electron scattering simulations based on electrostatic potentials obtained from first principles, mainly via density functional theory, which was previously mainly required for holography. In this Review, we discuss the motivation and basis for these developments, survey the pioneering work that has been published thus far, and give our outlook for the future. We argue that a physically better justified $\text{ab initio}$ description of the scattering potential is both useful and viable for an increasing number of systems, and we expect such simulations to steadily gain in popularity and importance.

arXiv comment41 pages, 5 figures, 3 tables, 137 references

References in corpus (14)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A real-space grid implementation of the Projector Augmented Wave method
• Electron ptychography achieves atomic-resolution limits set by lattice vibrations
• Localized atomic basis set in the projector augmented wave method
• Position and momentum mapping of vibrations in graphene nanostructures in the electron microscope
• Single-atom spectroscopy of phosphorus dopants implanted into graphene
• Unraveling the 3D atomic structure of a suspended graphene/hBN van der Waals heterostructure
• Efficient and versatile model for vibrational STEM-EELS
• Efficient first principles simulation of electron scattering factors for transmission electron microscopy
• libvdwxc: A library for exchange-correlation functionals in the vdW-DF family
• Mapping atomic orbitals with the transmission electron microscope: Images of defective graphene predicted from first-principles theory
• A linear-scaling algorithm for rapid computation of inelastic transitions in the presence of multiple electron scattering
• The Effect of Intra-Layer Bonding on Electron-Optical Phase Images of Few-Layer WSe2
• Direct Imaging of Charge Redistribution due to Bonding at Atomic Resolution via Electron Ptychography

Cited by in corpus (5)

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• Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwell time and faster scans for high precision and low dose applications
• Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science
• Uncovering the atomic structure of substitutional platinum dopants in MoS${}_2$ with single-sideband ptychography
• Enhancing atomic-resolution in electron microscopy: A frequency-domain deep learning denoiser