430 citations · 1.7k across the 114 of their papers we have counts for
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Automated experiment in 4D-STEM: exploring emergent physics and structural behaviors
Kevin M. Roccapriore, Ondrej Dyck, Mark P. Oxley +2
Automated experiments in 4D Scanning Transmission Electron Microscopy are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric…
Exploring causal physical mechanisms via non-gaussian linear models and deep kernel learning: applications for ferroelectric domain structures
Yongtao Liu, Maxim Ziatdinov, Sergei V. Kalinin
Rapid emergence of the multimodal imaging in scanning probe, electron, and optical microscopies have brought forth the challenge of understanding the information contained in these…
Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models
Sergei V. Kalinin, Ayana Ghosh, Rama Vasudevan +1
The development of high-resolution imaging methods such as electron and scanning probe microscopy and atomic probe tomography have provided a wealth of information on structure and…
Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline
Ayana Ghosh, Maxim Ziatdinov, Ondrej Dyck +2
Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally o…
Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications
Arpan Biswas, Anna N. Morozovska, Maxim Ziatdinov +2
Optimization of materials performance for specific applications often requires balancing multiple aspects of materials functionality. Even for the cases where generative physical m…
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process
Maxim Ziatdinov, Ayana Ghosh, Sergei V. Kalinin
Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces…
Experimental discovery of structure-property relationships in ferroelectric materials via active learning
Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan +3
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics…
Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
Kevin M. Roccapriore, Sergei V. Kalinin, Maxim Ziatdinov
Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement a dee…
Towards Automating Structural Discovery in Scanning Transmission Electron Microscopy
Nicole Creange, Ondrej Dyck, Rama K. Vasudevan +2
Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in…
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders
Maxim Ziatdinov, Chun Yin Wong, Sergei V. Kalinin
Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information…
High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches
Kate Higgins, Maxim Ziatdinov, Sergei V. Kalinin +1
Antisolvent crystallization methods are frequently used to fabricate high-quality perovskite thin films, to produce sizable single crystals, and to synthesize nanoparticles at room…
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries
Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu +2
The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanopar…
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond
Maxim Ziatdinov, Ayana Ghosh, Tommy Wong +1
AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applicati…
Disentangling ferroelectric wall dynamics and identification of pinning mechanisms via deep learning
Yongtao Liu, Roger Proksch, Chun Yin Wong +2
Field-induced domain wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitati…
Exploring Electron Beam Induced Atomic Assembly via Reinforcement Learning in a Molecular Dynamics Environment
Rama K. Vasudevan, Ayana Ghosh, Maxim Ziatdinov +1
Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanoscience and nanotechnology. While traditionally implemented via scannin…
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…
Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms
Maxim Ziatdinov, Sergei Kalinin
Recent advances in imaging from celestial objects in astronomy visualized via optical and radio telescopes to atoms and molecules resolved via electron and probe microscopes are ge…
Revealing the chemical bonding in adatoms arrays via machine learning of 3D scanning tunneling spectroscopy data
Kevin M. Roccapriore, Qiang Zou, Lizhi Zhang +9
The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information…
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy
Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle +6
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials pred…
Mapping causal patterns in crystalline solids
Chris Nelson, Anna N. Morozovska, Maxim A. Ziatdinov +4
The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase…
Deep learning polarization distributions in ferroelectrics from STEM data: with and without atom finding
Ayana Ghosh, Christopher T. Nelson, Mark Oxley +4
Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, o…
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy
Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck +2
Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotempor…
Unsupervised learning of ferroic variants from atomically resolved STEM images
Mani Valleti, Sergei V. Kalinin, Christopher T. Nelson +6
An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chem…