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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
arXiv.org
04 Apr 2023
Abstract
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-centered
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 the 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 and 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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Details
- Title
- Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
- Creators
- Sergei V KalininDebangshu MukherjeeKevin M RoccaprioreBen BlaiszikAyana GhoshMaxim A ZiatdinovA Al-NajjarChristina DotySarah AkersNageswara S RaoJoshua C AgarSteven R Spurgeon
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Other Identifier
- 991021878112504721