109 citations · 519 across the 47 of their papers we have counts for
20 papers · 1 filter
Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy
Yongtao Liu, Jonghee Yang, Rama K. Vasudevan +4
Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These p…
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.…
Delineating complex ferroelectric domain structures via second harmonic generation spectral imaging
Wei Li, Yunpeng Ma, Tianyi Feng +3
Understanding the mechanisms and spatial correlations of crystallographic symmetry breaking in ferroelectric materials is essential to tuning their functional properties. While opt…
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…
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies
Zhuowen Zhao, Tanny Chavez, Elizabeth A. Holman +14
Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data p…
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…
Learning the right channel in multimodal imaging: automated experiment in Piezoresponse Force Microscopy
Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley +3
We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest i…
Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach
Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov +1
Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences du…
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…
Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy
Yongtao Liu, Kyle P. Kelley, Hiroshi Funakubo +2
The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in-situ implementation of computer vision algorithms in scanning probe mi…
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning
Maxim Ziatdinov, Yongtao Liu, Kyle Kelley +2
Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous…
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…
Physical discovery in representation learning via conditioning on prior knowledge: applications for ferroelectric domain dynamics
Yongtao Liu, Bryan D Huey, Maxim A. Ziatdinov +1
Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of com…
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model
Rama K. Vasudevan, Erick Orozco, Sergei V. Kalinin
The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases m…
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials
Yongtao Liu, Anna Morozovska, Eugene Eliseev +4
We report the development and implementation of a hypothesis learning based automated experiment, in which the microscope operating in the autonomous mode identifies the physical l…
Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels
Kevin M. Roccapriore, Maxim Ziatdinov, Andrew R. Lupini +3
Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visuali…