Automatic parameter selection for electron ptychography via Bayesian optimization

Cao, Michael C.; Chen, Zhe; Jiang, Yi; Han, Yimo

doi:10.1038/s41598-022-16041-5

Cited by 8 publications

(1 citation statement)

References 55 publications

(65 reference statements)

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“…We efficiently explored the parameter space with Bayesian optimization (BO) -a machine learning framework known for optimizing expensive unknown objective functions with minimal evaluations [57,58]. It works by building a probabilistic model of the objective function with Gaussian processes (GP) regression.…”

Section: Multi-modal Simulations and Bayesian Hyperparameter Optimiza...mentioning

confidence: 99%

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Hovden

Schwartz

et al. 2023

Preprint

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Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

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Section: Multi-modal Simulations and Bayesian Hyperparameter Optimiza...mentioning

confidence: 99%

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Hovden

Schwartz

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography

Schwartz,

Di,

Jiang

et al. 2024

Nat Commun

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Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe3O4 metamaterial within an organic ligand matrix, Co3O4-Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

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Machine learning for automated experimentation in scanning transmission electron microscopy

Kalinin,

Mukherjee,

Roccapriore

et al. 2023

npj Comput Mater

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Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

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Automatic parameter selection for electron ptychography via Bayesian optimization

Cited by 8 publications

References 55 publications

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography

Machine learning for automated experimentation in scanning transmission electron microscopy

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