430 citations · 535 across the 22 of their papers we have counts for
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Deep Bayesian Local Crystallography
Sergei V. Kalinin, Mark P. Oxley, Mani Valleti +7
The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and…
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
Mark P. Oxley, Maxim Ziatdinov, Ondrej Dyck +3
The 4D scanning transmission electron microscopy (STEM) method has enabled mapping of the structure and functionality of solids on the atomic scale, yielding information-rich data…
Application of variational policy gradient to atomic-scale materials synthesis
Siyan Liu, Nikolay Borodinov, Lukas Vlcek +3
Atomic-scale materials synthesis via layer deposition techniques present a unique opportunity to control material structures and yield systems that display unique functional proper…
Gaussian process analysis of Electron Energy Loss Spectroscopy (EELS) data: parallel reconstruction and kernel control
Sergei V. Kalinin, Andrew R. Lupini, Rama K. Vasudevan +1
Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of explora…
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality
Rama K. Vasudevan, Maxim Ziatdinov, Lukas Vlcek +1
Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, a…
Bayesian inference in band excitation Scanning Probe Microscopy for optimal dynamic model selection in imaging
Rama K. Vasudevan, Kyle P. Kelley, Eugene Eliseev +4
The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. Th…