activity
20172022
most citedDeep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

430 citations · 535 across the 22 of their papers we have counts for

collaborators
Showing cond-mat.mtrl-sciShow all

18 papers · 1 filter

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

Bayesian learning of adatom interactions from atomically-resolved imaging data

Mani Valleti, Qiang Zou, Rui Xue +8

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be capture…

cond-mat.mtrl-sci20201 cited

Latent mechanisms of polarization switching from in situ electron microscopy observations

Reinis Ignatans, Maxim Ziatdinov, Rama Vasudevan +3

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. Th…

cond-mat.mtrl-sci2020

Fast Scanning Probe Microscopy via Machine Learning: Non-rectangular scans with compressed sensing and Gaussian process optimization

Kyle P. Kelley, Maxim Ziatdinov, Liam Collins +5

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functiona…

cond-mat.mtrl-sci2020

Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

Christopher T. Nelson, Rama K. Vasudevan, Xiaohang Zhang +5

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ult…

cond-mat.mtrl-sci2020

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation

Sergei V. Kalinin, Mani Valleti, Rama K. Vasudevan +1

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics, that have allowed both decades of scientific disc…