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20222024
most citedDeep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

109 citations · 133 across the 6 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

4 papers · 1 filter

cond-mat.mtrl-sci2024★ 1 cited

Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision

Arman Ter-Petrosyan, Michael Holden, Jenna A. Bilbrey +11

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microst…

cond-mat.mtrl-sci2024★ 11 cited

SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation

Waqwoya Abebe, Jan Strube, Luanzheng Guo +5

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries locate…

cond-mat.mtrl-sci2023★ 1 cited

Unsupervised segmentation of irradiation$\unicode{x2010}$induced order$\unicode{x2010}$disorder phase transitions in electron microscopy

Arman H Ter-Petrosyan, Jenna A Bilbrey, Christina M Doty +6

We present a method for the unsupervised segmentation of electron microscopy images, which are powerful descriptors of materials and chemical systems. Images are oversegmented into…

cond-mat.mtrl-sci2023★ 109 cited

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore +9

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…