109 citations · 519 across the 47 of their papers we have counts for
4 papers · 1 filter
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…
Learning and predicting photonic responses of plasmonic nanoparticle assemblies via dual variational autoencoders
Muammer Y. Yaman, Sergei V. Kalinin, Kathryn N. Guye +2
We demonstrate the application of machine learning for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational…
Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy
Kevin M. Roccapriore, Matthew G. Boebinger, Ondrej Dyck +4
The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of dee…
Bayesian Optimization in Continuous Spaces via Virtual Process Embeddings
Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov +1
Automated chemical synthesis, materials fabrication, and spectroscopic physical measurements often bring forth the challenge of process trajectory optimization, i.e., discovering t…