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Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Machine learning for automated experimentation in scanning transmission electron microscopy
Abstract Machine learning (ML) has become critical for post-acquisition data analysis in
(scanning) transmission electron microscopy,(S) TEM, imaging and spectroscopy. An …
(scanning) transmission electron microscopy,(S) TEM, imaging and spectroscopy. An …
Optical ptychography for biomedical imaging: recent progress and future directions
Ptychography is an enabling microscopy technique for both fundamental and applied
sciences. In the past decade, it has become an indispensable imaging tool in most X-ray …
sciences. In the past decade, it has become an indispensable imaging tool in most X-ray …
[HTML][HTML] Machine learning on neutron and x-ray scattering and spectroscopies
Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …
characterization techniques that measure materials structural and dynamical properties with …
Applications of deep learning in electron microscopy
We review the growing use of machine learning in electron microscopy (EM) driven in part
by the availability of fast detectors operating at kiloHertz frame rates leading to large data …
by the availability of fast detectors operating at kiloHertz frame rates leading to large data …
Deep learning at the edge enables real-time streaming ptychographic imaging
Coherent imaging techniques provide an unparalleled multi-scale view of materials across
scientific and technological fields, from structural materials to quantum devices, from …
scientific and technological fields, from structural materials to quantum devices, from …
Deep-learning electron diffractive imaging
We report the development of deep-learning coherent electron diffractive imaging at
subangstrom resolution using convolutional neural networks (CNNs) trained with only …
subangstrom resolution using convolutional neural networks (CNNs) trained with only …
A review of image-based simulation applications in high-value manufacturing
Abstract Image-Based Simulation (IBSim) is the process by which a digital representation of
a real geometry is generated from image data for the purpose of performing a simulation …
a real geometry is generated from image data for the purpose of performing a simulation …
PtyLab. m/py/jl: a cross-platform, open-source inverse modeling toolbox for conventional and Fourier ptychography
Conventional (CP) and Fourier (FP) ptychography have emerged as versatile quantitative
phase imaging techniques. While the main application cases for each technique are …
phase imaging techniques. While the main application cases for each technique are …
AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging
The problem of phase retrieval underlies various imaging methods from astronomy to
nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore …
nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore …