19 citations · 39 across the 12 of their papers we have counts for
14 papers
Disentangling electronic transport and hysteresis at individual grain boundaries in hybrid perovskites via automated scanning probe microscopy
Yongtao Liu, Jonghee Yang, Benjamin J. Lawrie +4
Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advance in the understanding of the microstructure of polycrystall…
Enabling Autonomous Electron Microscopy for Networked Computation and Steering
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran +6
Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and auto…
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…
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 is the New Data
Sergei V. Kalinin, Maxim Ziatdinov, Bobby G. Sumpter +1
The rapid development of machine learning (ML) methods has fundamentally affected numerous applications ranging from computer vision, biology, and medicine to accounting and text a…
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning
Maxim Ziatdinov, Yongtao Liu, Sergei V. Kalinin
Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation. The distinctive aspect of ma…