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Title: Fast approximate STEM image simulations from a machine learning model

Abstract

Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 – R 2 error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.

Authors:
 [1];  [1];  [1];  [1];  [1]; ORCiD logo [1]
  1. Univ. of Wisconsin, Madison, WI (United States). Dept. of Materials Science and Engineering
Publication Date:
Research Org.:
Univ. of Wisconsin, Madison, WI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1557601
Grant/Contract Number:  
FG02-08ER46547
Resource Type:
Accepted Manuscript
Journal Name:
Advanced Structural and Chemical Imaging
Additional Journal Information:
Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 2198-0926
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Combs, Aidan H., Maldonis, Jason J., Feng, Jie, Xu, Zhongnan, Voyles, Paul M., and Morgan, Dane. Fast approximate STEM image simulations from a machine learning model. United States: N. p., 2019. Web. doi:10.1186/s40679-019-0064-2.
Combs, Aidan H., Maldonis, Jason J., Feng, Jie, Xu, Zhongnan, Voyles, Paul M., & Morgan, Dane. Fast approximate STEM image simulations from a machine learning model. United States. doi:10.1186/s40679-019-0064-2.
Combs, Aidan H., Maldonis, Jason J., Feng, Jie, Xu, Zhongnan, Voyles, Paul M., and Morgan, Dane. Tue . "Fast approximate STEM image simulations from a machine learning model". United States. doi:10.1186/s40679-019-0064-2. https://www.osti.gov/servlets/purl/1557601.
@article{osti_1557601,
title = {Fast approximate STEM image simulations from a machine learning model},
author = {Combs, Aidan H. and Maldonis, Jason J. and Feng, Jie and Xu, Zhongnan and Voyles, Paul M. and Morgan, Dane},
abstractNote = {Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 – R2 error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.},
doi = {10.1186/s40679-019-0064-2},
journal = {Advanced Structural and Chemical Imaging},
number = 1,
volume = 5,
place = {United States},
year = {2019},
month = {3}
}

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    Works referencing / citing this record:

    Simulation of scanning transmission electron microscope images on desktop computers
    journal, February 2010


    Ultrafast electron diffraction pattern simulations using GPU technology. Applications to lattice vibrations
    journal, November 2013


    Probe integrated scattering cross sections in the analysis of atomic resolution HAADF STEM images
    journal, October 2013


    A memory efficient method for fully three-dimensional object reconstruction with HAADF STEM
    journal, June 2014


    Benchmark test of accelerated multi-slice simulation by GPGPU
    journal, November 2015


    FDES, a GPU-based multislice algorithm with increased efficiency of the computation of the projected potential
    journal, November 2015


    STEM image simulation with hybrid CPU/GPU programming
    journal, July 2016


    Integrated Computational and Experimental Structure Refinement for Nanoparticles
    journal, April 2016


    Rapid Estimation of Catalyst Nanoparticle Morphology and Atomic-Coordination by High-Resolution Z-Contrast Electron Microscopy
    journal, October 2014

    • Jones, Lewys; MacArthur, Katherine E.; Fauske, Vidar T.
    • Nano Letters, Vol. 14, Issue 11
    • DOI: 10.1021/nl502762m

    Atom-by-atom structural and chemical analysis by annular dark-field electron microscopy
    journal, March 2010

    • Krivanek, Ondrej L.; Chisholm, Matthew F.; Nicolosi, Valeria
    • Nature, Vol. 464, Issue 7288
    • DOI: 10.1038/nature08879

    Electron tomography at 2.4-ångström resolution
    journal, March 2012

    • Scott, M. C.; Chen, Chien-Chun; Mecklenburg, Matthew
    • Nature, Vol. 483, Issue 7390
    • DOI: 10.1038/nature10934

    Deciphering chemical order/disorder and material properties at the single-atom level
    journal, February 2017

    • Yang, Yongsoo; Chen, Chien-Chun; Scott, M. C.
    • Nature, Vol. 542, Issue 7639
    • DOI: 10.1038/nature21042

    Simultaneous atomic-resolution electron ptychography and Z-contrast imaging of light and heavy elements in complex nanostructures
    journal, August 2016

    • Yang, H.; Rutte, R. N.; Jones, L.
    • Nature Communications, Vol. 7, Issue 1
    • DOI: 10.1038/ncomms12532

    Picometre-precision analysis of scanning transmission electron microscopy images of platinum nanocatalysts
    journal, June 2014

    • Yankovich, Andrew B.; Berkels, Benjamin; Dahmen, W.
    • Nature Communications, Vol. 5, Issue 1
    • DOI: 10.1038/ncomms5155

    Three-dimensional coordinates of individual atoms in materials revealed by electron tomography
    journal, September 2015

    • Xu, Rui; Chen, Chien-Chun; Wu, Li
    • Nature Materials, Vol. 14, Issue 11
    • DOI: 10.1038/nmat4426

    Low-dose cryo electron ptychography via non-convex Bayesian optimization
    journal, August 2017


    Variable-angle high-angle annular dark-field imaging: application to three-dimensional dopant atom profiling
    journal, July 2015

    • Zhang, Jack Y.; Hwang, Jinwoo; Isaac, Brandon J.
    • Scientific Reports, Vol. 5, Issue 1
    • DOI: 10.1038/srep12419

    Simulation of high angle annular dark field scanning transmission electron microscopy images of large nanostructures
    journal, October 2008

    • Pizarro, J.; Galindo, P. L.; Guerrero, E.
    • Applied Physics Letters, Vol. 93, Issue 15
    • DOI: 10.1063/1.2998656

    Fourier Images: I - The Point Source
    journal, May 1957


    Atomic electron tomography: 3D structures without crystals
    journal, September 2016


    A fast image simulation algorithm for scanning transmission electron microscopy
    journal, May 2017