62 citations · 144 across the 6 of their papers we have counts for
6 papers
Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data
Maxim Ziatdinov, Shuai Zhang, Orion Dollar +7
Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the cl…
Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data
Xin Li, Ondrej Dyck, Stephen Jesse +3
Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras a…
Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy
Xin Li, Ondrej E. Dyck, Mark P. Oxley +5
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomi…
Building and exploring libraries of atomic defects in graphene: scanning transmission electron and scanning tunneling microscopy study
Maxim Ziatdinov, Ondrej Dyck, Bobby G. Sumpter +3
Population and distribution of defects is one of the primary parameters controlling materials functionality, are often non-ergodic and strongly dependent on synthesis history, and…
Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) on Non-Rectangular Scans
Xin Li, Ondrej Dyck, Sergei V. Kalinin +1
Scanning Transmission Electron Microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized…
Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images
Xin Li, Alex Belianinov, Ondrej Dyck +2
This paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microsc…