Neural-network-based depth-resolved multiscale structural optimization using density functional theory and electron diffraction data

Iterative neural-network-based three-dimensional structural optimization of atomic positions over tens of nanometers is performed using transmission electron microscope (TEM) diffraction data simulated from density functional theory (DFT) all-electron densities, thus retrieving parameter variations...

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Bibliographic Details
Published in:Physical review. B 2018-01, Vol.97 (2), Article 024112
Main Authors: Pennington, Robert S., Coll, Catalina, Estradé, Sònia, Peiró, Francesca, Koch, Christoph T.
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Density functional theory
Diffraction
Diffraction patterns
Domains
Electron diffraction
Ferroelectric materials
Ferroelectricity
Iterative methods
Mathematical analysis
Neural networks
Optimization
Parameters
Polarization
Production methods
Scattering
Transmission electron microscopy
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Summary:Iterative neural-network-based three-dimensional structural optimization of atomic positions over tens of nanometers is performed using transmission electron microscope (TEM) diffraction data simulated from density functional theory (DFT) all-electron densities, thus retrieving parameter variations along the beam direction. We first use experimental data to show that the GPAW DFT code's all-electron densities are considerably more accurate for electron diffraction calculations compared to conventional isolated-atom scattering factors, and they also compare well to Wien2K DFT simulations. This DFT-TEM combination is then integrated into an iterative neural-network-optimization-based algorithm (PRIMES, parameter retrieval and inversion from multiple electron scattering) to retrieve nanometer-scale ferroelectric polarization domains and strain in theoretical bulklike specimens from TEM data. DFT and isolated-atom methods produce substantially different diffraction patterns and retrieved polarization domain parameters, and DFT is sufficient to retrieve strain properties from a silicon specimen simulated using experimentally derived structure factors. Thus, we show that the improved accuracy, fast computation, and intuitive integration make the GPAW DFT code well suited for three-dimensional materials characterization and demonstrate this using an iterative neural-network algorithm that is verifiable on the mesoscale and, with DFT integration, self-consistent on the nanoscale.
ISSN:2469-9950
2469-9969
DOI:10.1103/PhysRevB.97.024112