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20182022
most citedDeep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

430 citations · 484 across the 8 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

12 papers · 1 filter

cond-mat.mtrl-sci202243 cited

Electron-beam Introduction of Heteroatomic Pt-Si Structures in Graphene

Ondrej Dyck, Cheng Zhang, Philip D. Rack +5

Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion a…

cond-mat.mtrl-sci2021

Towards Automating Structural Discovery in Scanning Transmission Electron Microscopy

Nicole Creange, Ondrej Dyck, Rama K. Vasudevan +2

Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in…

cond-mat.mtrl-sci2020

Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data

Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh +4

We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomicall…

cond-mat.mtrl-sci2020

Exploring order parameters and dynamic processes in disordered systems via variational autoencoders

Sergei V. Kalinin, Ondrej Dyck, Stephen Jesse +1

We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolve…

cond-mat.mtrl-sci2019

Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk

Maxim Ziatdinov, Ondrej Dyck, Stephen Jesse +1

Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron…

cond-mat.mtrl-sci2018

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Maxim Ziatdinov, Stephen Jesse, Bobby G. Sumpter +2

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was…