109 citations · 345 across the 26 of their papers we have counts for
28 papers
Quantifying the coupling between strain and cation valence in high entropy oxide thin films using electron microscopy
Sai Venkata Gayathri Ayyagari, Saeed SI Almishal, Debangshu Mukherjee +3
High entropy oxides (HEOs) are a class of materials with vast compositional space and tunable properties, making them attractive for applications in thermoelectrics, magnetism, ion…
Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop
Lance Yao, Suman Samantray, Ayana Ghosh +4
The history of science is punctuated by serendipitous discoveries, where unexpected observations, rather than targeted hypotheses, opened new fields of inquiry. While modern autono…
Autonomous Fabrication of Tailored Defect Structures in 2D Materials using Machine Learning-enabled Scanning Transmission Electron Microscopy
Zijie Wu, Kevin M. Roccapriore, Ayana Ghosh +5
Materials with tailored quantum properties can be engineered from atomic scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intell…
Quantitative electron beam-single atom interactions enabled by sub-20-pm precision targeting
Kevin M. Roccapriore, Frances M. Ross, Julian Klein
The ability to probe and control matter at the picometer scale is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy offers powerful…
Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS
Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu +3
Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron e…
AEcroscoPy: A software-hardware framework empowering microscopy toward automated and autonomous experimentation
Yongtao Liu, Kevin Roccapriore, Marti Checa +4
Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now…
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer, Jesse Farebrother, Joshua Greaves +8
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning…
Dynamic STEM-EELS for single atom and defect measurement during electron beam transformations
Kevin M. Roccapriore, Riccardo Torsi, Joshua Robinson +2
On- and off-axis electron energy loss spectroscopy (EELS) is a powerful method for probing local electronic structure on single atom level. However, many materials undergo electron…
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…
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas +5
Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-…
Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation
Sumner B. Harris, Arpan Biswas, Seok Joon Yun +5
Synthesis of thin films has traditionally relied upon slow, sequential processes carried out with substantial human intervention, frequently utilizing a mix of experience and seren…
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…
Photon bunching in cathodoluminescence induced by indirect electron excitation
Vasudevan Iyer, Kevin Roccapriore, Jacob Ng +3
The impulsive excitation of ensembles of excitons or color centers by a high-energy electron beam results in the observation of photon bunching in the second-order correlation func…
Enabling Autonomous Electron Microscopy for Networked Computation and Steering
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran +6
Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and auto…
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…
Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels
Kevin M. Roccapriore, Maxim Ziatdinov, Andrew R. Lupini +3
Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visuali…
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…
Electron-beam induced emergence of mesoscopic ordering in layered MnPS
Kevin M. Roccapriore, Nan Huang, Mark P. Oxley +8
Ordered mesoscale structures in 2D materials induced by small misorientations have opened pathways for a wide variety of novel electronic, ferroelectric, and quantum phenomena. Unt…
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…
Sculpting the plasmonic responses of nanoparticles by directed electron beam irradiation
Kevin M. Roccapriore, Shin-Hum Cho, Andrew R. Lupini +2
Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomen…
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…