138 citations · 392 across the 23 of their papers we have counts for
23 papers
Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings
Ayana Ghosh, Maxim Ziatdinov, Sergei V. Kalinin
As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular propertie…
When the atoms dance: exploring mechanisms of electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy
Matthew G. Boebinger, Ayana Ghosh, Kevin M. Roccapriore +6
Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In thi…
Bending-induced isostructural transitions in ultrathin layers of van der Waals ferrielectrics
Anna N. Morozovska, Eugene A. Eliseev, Yongtao Liu +8
Using Landau-Ginzburg-Devonshire (LGD) phenomenological approach we analyze the bending-induced re-distribution of electric polarization and field, elastic stresses and strains ins…
Anomalous Polarization Reversal in Strained Thin Films of CuInPS
Anna N. Morozovska, Eugene A. Eliseev, Ayana Ghosh +3
Strain-induced transitions of polarization reversal in thin films of a ferrielectric CuInPS (CIPS) with ideally-conductive electrodes is explored using the Landau-Ginzburg-…
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore +9
Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is…
Designing Workflows for Materials Characterization
Sergei V. Kalinin, Maxim Ziatdinov, Mahshid Ahmadi +4
Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characteriz…
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space
Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov
Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development…
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.…
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…
Insights into cation ordering of double perovskite oxides from machine learning and causal relations
Ayana Ghosh, Gayathri Palanichamy, Dennis P. Trujillo +2
This work investigates the origins of cation ordering of double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We…
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…
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…
Observation of topological surface state in a superconducting material
Gyanendra Dhakal, M. Mofazzel Hosen, Ayana Ghosh +14
The discovery of topological insulator phase has ignited massive research interests in novel quantum materials. Topological insulators with superconductivity further invigorate the…
Understanding Magnetic Properties of Actinide-Based Compounds from Machine Learning
Ayana Ghosh, Filip Ronning, Serge Nakhmanson +1
Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f-electrons and include unconventional superconductivit…