138 citations · 392 across the 23 of their papers we have counts for
9 papers · 1 filter
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…
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…
Identification of novel organic ferroelectrics: A study combining importance sampling with machine learning
Ayana Ghosh, Dennis P. Trujillo, Subhashis Hazarika +5
Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for their incorporatio…
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…
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…
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…
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…