430 citations · 699 across the 63 of their papers we have counts for
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Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process
Maxim Ziatdinov, Ayana Ghosh, Sergei V. Kalinin
Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces…
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…
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…
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…