430 citations · 1.3k across the 32 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Reconstruction of effective potential from statistical analysis of dynamic trajectories
Ali Yousefzadi Nobakht, Ondrej Dyck, David B. Lingerfelt +8
The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in…