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 …
Automated and autonomous experiments in electron and scanning probe microscopy
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …
part of physics research, with domain applications ranging from theory and materials …
Deep learning object detection in materials science: Current state and future directions
R Jacobs - Computational Materials Science, 2022 - Elsevier
Deep learning-based object detection models have recently found widespread use in
materials science, with rapid progress made in just the past two years. Scanning and …
materials science, with rapid progress made in just the past two years. Scanning and …
Probing electron beam induced transformations on a single-defect level via automated scanning transmission electron microscopy
A robust approach for real-time analysis of the scanning transmission electron microscopy
(STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep …
(STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep …
Automated experiment in 4D-STEM: exploring emergent physics and structural behaviors
Automated experiments in 4D scanning transmission electron microscopy (STEM) are
implemented for rapid discovery of local structures, symmetry-breaking distortions, and …
implemented for rapid discovery of local structures, symmetry-breaking distortions, and …
Exploring physics of ferroelectric domain walls in real time: deep learning enabled scanning probe microscopy
The functionality of ferroelastic domain walls in ferroelectric materials is explored in real‐
time via the in situ implementation of computer vision algorithms in scanning probe …
time via the in situ implementation of computer vision algorithms in scanning probe …
Quantitative scanning transmission electron microscopy for materials science: Imaging, diffraction, spectroscopy, and tomography
C Ophus - Annual Review of Materials Research, 2023 - annualreviews.org
Scanning transmission electron microscopy (STEM) is one of the most powerful
characterization tools in materials science research. Due to instrumentation developments …
characterization tools in materials science research. Due to instrumentation developments …
Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns
A fast, robust pipeline for strain mapping of crystalline materials is important for many
technological applications. Scanning electron nanodiffraction allows us to calculate strain …
technological applications. Scanning electron nanodiffraction allows us to calculate strain …
Atomvision: A machine vision library for atomistic images
Computer vision techniques have immense potential for materials design applications. In
this work, we introduce an integrated and general-purpose AtomVision library that can be …
this work, we introduce an integrated and general-purpose AtomVision library that can be …
Exploring causal physical mechanisms via non-gaussian linear models and deep kernel learning: applications for ferroelectric domain structures
Rapid emergence of multimodal imaging in scanning probe, electron, and optical
microscopies has brought forth the challenge of understanding the information contained in …
microscopies has brought forth the challenge of understanding the information contained in …