Prismatic 2.0 – Simulation software for scanning and high resolution transmission electron microscopy (STEM and HRTEM)

DaCosta, Luis Rangel; Brown, Hamish G.; Pelz, Philipp; Rakowski, Alexander; Barber, Natolya; O′Donovan, Peter; McBean, Patrick; Jones, Lewys; Ciston, Jim; Scott, Mary; Ophus, Colin

doi:10.1016/j.micron.2021.103141

Cited by 46 publications

(23 citation statements)

References 38 publications

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“…We performed these simulations using the multislice algorithm (Cowley & Moodie, 1957), and methods defined by Kirkland (2020) and Ophus (2017). These methods are implemented in the simulation code by Rangel DaCosta et al (2021). The diffraction patterns were generated using a acceleration potential of 300 keV, a 0.5 mrad convergence semiangle, with real space and reciprocal pixel sizes of 0.05 and 0.01 , respectively, with four frozen phonons.…”

Section: Methodsmentioning

confidence: 99%

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

et al. 2022

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Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

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Section: Methodsmentioning

confidence: 99%

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

et al. 2022

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“…The two most commonly used image simulation methods are the Bloch wave and multislice methods, see ( Kirkland, 1998 ) for detailed descriptions. Some popular software options for STEM image simulation are Dr. Probe ( Barthel, 2018 ) Prismatic ( Ophus, 2017 ; Rangel DaCosta et al, 2021 ), and pyQSTEM ( Koch, 2002 ), among many others.…”

Section: Structure Analysis From Real-space Imagingmentioning

confidence: 99%

Probing Multiscale Disorder in Pyrochlore and Related Complex Oxides in the Transmission Electron Microscope: A Review

et al. 2021

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Transmission electron microscopy (TEM), and its counterpart, scanning TEM (STEM), are powerful materials characterization tools capable of probing crystal structure, composition, charge distribution, electronic structure, and bonding down to the atomic scale. Recent (S)TEM instrumentation developments such as electron beam aberration-correction as well as faster and more efficient signal detection systems have given rise to new and more powerful experimental methods, some of which (e.g., 4D-STEM, spectrum-imaging, in situ/operando (S)TEM)) facilitate the capture of high-dimensional datasets that contain spatially-resolved structural, spectroscopic, time- and/or stimulus-dependent information across the sub-angstrom to several micrometer length scale. Thus, through the variety of analysis methods available in the modern (S)TEM and its continual development towards high-dimensional data capture, it is well-suited to the challenge of characterizing isometric mixed-metal oxides such as pyrochlores, fluorites, and other complex oxides that reside on a continuum of chemical and spatial ordering. In this review, we present a suite of imaging and diffraction (S)TEM techniques that are uniquely suited to probe the many types, length-scales, and degrees of disorder in complex oxides, with a focus on disorder common to pyrochlores, fluorites and the expansive library of intermediate structures they may adopt. The application of these techniques to various complex oxides will be reviewed to demonstrate their capabilities and limitations in resolving the continuum of structural and chemical ordering in these systems.

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“…In our work, a convolutional neural network has been trained using HAADF-STEM images of particles, simulated using the plane-wave reciprocal-space interpolated scattering matrix (PRISM) algorithm (Figure 1 d-u), to recognize the different shapes and patterns of nanocluster images acquired in HAADF-STEM [6]. The neural network can be used to rapidly determine the proportion of different particle isomers.…”

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confidence: 99%

Classification of Metal Nanoclusters Using Convolutional Neural Networks

Dearg

Hoddinott

Niu

et al. 2022

Microscopy and Microanalysis

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Catalysis happens only at the surface of materials, this makes nanoparticles of particular interest in the field of catalysis because of their high surface-to-volume ratio. The exact atomic structure of nanoparticle surfaces is of particular importance in catalysis, and the expression of surface facets is largely governed by their overall structure. Typically, small metal nanoparticles will take one of three major structural isomers: decahedron, icosahedron or cuboctahedron (Figure 1). Determination of the structural isomer of a nanoparticle can be performed using high-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) [1,2].

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Prismatic 2.0 – Simulation software for scanning and high resolution transmission electron microscopy (STEM and HRTEM)

Cited by 46 publications

References 38 publications

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Probing Multiscale Disorder in Pyrochlore and Related Complex Oxides in the Transmission Electron Microscope: A Review

Classification of Metal Nanoclusters Using Convolutional Neural Networks

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