430 citations · 719 across the 43 of their papers we have counts for
5 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…
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…
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy
Yongtao Liu, Rama K. Vasudevan, Kyle Kelley +5
A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, d…
Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics
Rama K. Vasudevan, Kyle Kelley, Jacob Hinkle +4
Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy. A Bayesian Optimization framework for imaging is dev…
Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics
Sergei V. Kalinin, Maxim Ziatdinov, Rama K. Vasudevan
Advances in predictive modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials. Cor…