430 citations · 1.7k across the 114 of their papers we have counts for
28 papers · 1 filter
Bayesian learning of adatom interactions from atomically-resolved imaging data
Mani Valleti, Qiang Zou, Rui Xue +8
Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be capture…
Deep Bayesian Local Crystallography
Sergei V. Kalinin, Mark P. Oxley, Mani Valleti +7
The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and…
Latent mechanisms of polarization switching from in situ electron microscopy observations
Reinis Ignatans, Maxim Ziatdinov, Rama Vasudevan +3
In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. Th…
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…
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…
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
Mark P. Oxley, Maxim Ziatdinov, Ondrej Dyck +3
The 4D scanning transmission electron microscopy (STEM) method has enabled mapping of the structure and functionality of solids on the atomic scale, yielding information-rich data…
Predictability of localized plasmonic responses in nanoparticle assemblies
Kevin M. Roccapriore, Maxim Ziatdinov, Shin Hum Cho +2
Design of nanoscale structures with desired nanophotonic properties are key tasks for nanooptics and nanophotonics. Here, the correlative relationship between local nanoparticle ge…
Separating physically distinct mechanisms in complex infrared plasmonic nanostructures via machine learning enhanced electron energy loss spectroscopy
Sergei V. Kalinin, Kevin M. Roccapriore, Shin Hum Cho +4
Low-loss electron energy loss spectroscopy (EELS) has emerged as a technique of choice for exploring the localization of plasmonic phenomena at the nanometer level, necessitating a…
Identification and Correction of Temporal and Spatial Distortions in Scanning Transmission Electron Microscopy
Kevin M. Roccapriore, Nicole Creange, Maxim Ziatdinov +1
Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacemen…
Exploring particle dynamics during self-organization processes via rotationally invariant latent representations
Sergei V. Kalinin, Shuai Zhang, Mani Valleti +4
The dynamic of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines d…
Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning
Sergei V. Kalinin, James J. Steffes, Yongtao Liu +2
Domain switching pathways in ferroelectric materials visualized by dynamic Piezoresponse Force Microscopy (PFM) are explored via variational autoencoder (VAE), which simplifies the…
Investigating phase transitions from local crystallographic analysis based on machine learning of atomic environments
Rama K. Vasudevan, Maxim Ziatdinov, Lukas Vlcek +8
Traditionally, phase transitions are explored using a combination of macroscopic functional characterization and scattering techniques, providing insight into average properties an…
Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data
Maxim Ziatdinov, Shuai Zhang, Orion Dollar +7
Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the cl…
Exploring order parameters and dynamic processes in disordered systems via variational autoencoders
Sergei V. Kalinin, Ondrej Dyck, Stephen Jesse +1
We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolve…
Gaussian process analysis of Electron Energy Loss Spectroscopy (EELS) data: parallel reconstruction and kernel control
Sergei V. Kalinin, Andrew R. Lupini, Rama K. Vasudevan +1
Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of explora…
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality
Rama K. Vasudevan, Maxim Ziatdinov, Lukas Vlcek +1
Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, a…
Fast Scanning Probe Microscopy via Machine Learning: Non-rectangular scans with compressed sensing and Gaussian process optimization
Kyle P. Kelley, Maxim Ziatdinov, Liam Collins +5
Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functiona…
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…
Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
Christopher T. Nelson, Rama K. Vasudevan, Xiaohang Zhang +5
The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ult…
Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation
Sergei V. Kalinin, Mani Valleti, Rama K. Vasudevan +1
Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics, that have allowed both decades of scientific disc…
Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution Scanning Transmission Electron Microscopy data
Maxim Ziatdinov, Chris Nelson, Xiaohang Zhang +5
Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the a…
Predictability as a probe of manifest and latent physics: The case of atomic scale structural, chemical, and polarization behaviors in multiferroic Sm-doped BiFeO3
Maxim Ziatdinov, Nicole Creange, Xiaohang Zhang +6
The predictability of a certain effect or phenomenon is often equated with the knowledge of relevant physical laws, typically understood as a functional or numerically derived rela…
Reconstruction of effective potential from statistical analysis of dynamic trajectories
Ali Yousefzadi Nobakht, Ondrej Dyck, David B. Lingerfelt +8
The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in…
Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening
Mark P. Oxley, Junqi Yin, Nikolay Borodinov +6
Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron mic…