activity
20192022
most citedA roadmap for edge computing enabled automated multidimensional transmission electron microscopy

19 citations · 29 across the 6 of their papers we have counts for

collaborators

8 papers

cond-mat.dis-nn2022

Microscopy is All You Need

Sergei V. Kalinin, Rama Vasudevan, Yongtao Liu +3

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tr…

cond-mat.mtrl-sci202219 cited

A roadmap for edge computing enabled automated multidimensional transmission electron microscopy

Debangshu Mukherjee, Kevin M. Roccapriore, Anees Al-Najjar +8

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine.…

physics.comp-ph20211 cited

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…

cs.LG20214 cited

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle +6

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials pred…

physics.data-an20215 cited

Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck +2

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotempor…

cond-mat.mtrl-sci2020

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…