138 citations · 392 across the 23 of their papers we have counts for
11 papers · 1 filter
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…
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.…
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…
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…
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…
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…