activity
20202026
most citedDeep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

109 citations · 281 across the 23 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.DC2022★ 1 cited

Enabling Autonomous Electron Microscopy for Networked Computation and Steering

Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran +6

Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and auto…

cond-mat.dis-nn2022

Microscopy is All You Need

Sergei V. Kalinin, Rama Vasudevan, Yongtao Liu +3

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tr…

cond-mat.mtrl-sci2022★ 19 cited

A roadmap for edge computing enabled automated multidimensional transmission electron microscopy

Debangshu Mukherjee, Kevin M. Roccapriore, Anees Al-Najjar +8

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.…

cond-mat.dis-nn2022★ 1 cited

Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy

Kevin M. Roccapriore, Matthew G. Boebinger, Ondrej Dyck +4

The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of dee…

cond-mat.mtrl-sci2022

Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels

Kevin M. Roccapriore, Maxim Ziatdinov, Andrew R. Lupini +3

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visuali…