Free, flexible and fast: Orientation mapping using the multi-core and GPU-accelerated template matching capabilities in the Python-based open source 4D-STEM analysis toolbox Pyxem

Cautaerts, Niels; Crout, Phillip; Ånes, Håkon Wiik; Prestat, Éric; Jeong, Jiwon; Dehm, Gerhard; Liebscher, Christian

doi:10.1016/j.ultramic.2022.113517

Cited by 28 publications

(18 citation statements)

References 25 publications

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“…Equation ( 6) does not have a closed-form solution, so can be solved by iteration using different algorithms. The algorithm used in this workflow is built into PyXem 16 .…”

Section: Non-negative Matrix Factorisationmentioning

confidence: 99%

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Versatile domain mapping of scanning electron nanobeam diffraction datasets utilising variational autoencoders

et al. 2023

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Characterisation of structure across the nanometre scale is key to bridging the gap between the local atomic environment and macro-scale and can be achieved by means of scanning electron nanobeam diffraction (SEND). As a technique, SEND allows for a broad range of samples, due to being relatively tolerant of specimen thickness with low electron dosage. This, coupled with the capacity for automation of data collection over wide areas, allows for statistically representative probing of the microstructure. This paper outlines a versatile, data-driven approach for producing domain maps, and a statistical approach for assessing their applicability. The workflow utilises a Variational AutoEncoder to identify the sources of variance in the diffraction signal, and this, in combination with clustering techniques, is used to produce domain maps. This approach is agnostic to domain crystallinity, requires no prior knowledge of crystal structure, and does not require simulation of a library of expected diffraction patterns.

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“…Equation ( 6) does not have a closed-form solution, so can be solved by iteration using different algorithms. The algorithm used in this workflow is built into PyXem 16 .…”

Section: Non-negative Matrix Factorisationmentioning

confidence: 99%

“…Research into automated crystallographic orientation mapping (ACOM) 15,16 has employed SEND data collection to tackle the task of domain mapping. These techniques rely on prior knowledge of possible underlying crystal structures in order to classify the nature of individual diffraction patterns and then aggregate these into maps.…”

Section: Introductionmentioning

confidence: 99%

Versatile domain mapping of scanning electron nanobeam diffraction datasets utilising variational autoencoders

et al. 2023

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“…For instance, if our model is devoted to crystallographic analysis or phase identification, we can use the powerful CrysTBox for a quick validation. 468 If our model is for 4D-STEM data processing, we may additionally use Py4DSTEM 469 or Pyxem [470][471][472] for corroboration. In case our developed model tries to automate a particular step of the TEM data acquisition, maybe combining it with the ANIMATED-TEM toolbox allows us to reach the next level of overall automation.…”

Section: Training and Testingmentioning

confidence: 99%

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

2022

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“…Several recent open-source software packages have been developed to enable this. Some examples include STEMTool [42,43], py4DSTEM [44], Pycroscopy [45], PyXem [46], pixSTEM [47,48], and LiberTEM [49]. Each package focuses on a specific area of TEM analysis, such as Pycroscopy's focus on image processing or py4DSTEM's focus on 4D-STEM data analysis.…”

Section: Quantitative Analysis From Electron Microscopy Datasetsmentioning

confidence: 99%

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

et al. 2022

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Abstract:The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. Here we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

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Free, flexible and fast: Orientation mapping using the multi-core and GPU-accelerated template matching capabilities in the Python-based open source 4D-STEM analysis toolbox Pyxem

Cited by 28 publications

References 25 publications

Versatile domain mapping of scanning electron nanobeam diffraction datasets utilising variational autoencoders

Versatile domain mapping of scanning electron nanobeam diffraction datasets utilising variational autoencoders

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

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