119 citations · 392 across the 30 of their papers we have counts for
30 papers
Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors
Brad L. Boyce, Mitchell A. Wood, Krishna Garikipati +16
Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechani…
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
Bilvin Varughese, Troy D. Loeffler, Suvo Banik +9
The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture mul…
Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities
Michael H. Prince, Henry Chan, Aikaterini Vriza +6
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of ma…
Data-driven discovery of dynamics from time-resolved coherent scattering
Nina Andrejevic, Tao Zhou, Qingteng Zhang +3
Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. Howeve…
AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures
Arthur R. C. McCray, Tao Zhou, Saugat Kandel +3
The manipulation and control of nanoscale magnetic spin textures is of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving th…
AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources
Anakha V Babu, Tekin Bicer, Saugat Kandel +7
We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis o…
Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
Saugat Kandel, Tao Zhou, Anakha V Babu +7
With the continuing advances in scientific instrumentation, scanning microscopes are now able to image physical systems with up to sub-atomic-level spatial resolutions and sub-pico…
Multislice forward modeling of Coherent Surface Scattering Imaging on surface and interfacial structures
Peco Myint, Miaoqi Chu, Ashish Tripathi +6
To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle x-ray scattering are commonly used to yield…
Deep learning at the edge enables real-time streaming ptychographic imaging
Anakha V Babu, Tao Zhou, Saugat Kandel +15
Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from…
A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging
Sebastian Strempfer, Tao Zhou, Kazutomo Yoshii +6
Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific a…
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
Suvo Banik, Debdas Dhabal, Henry Chan +4
Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm th…
X-ray Nano-imaging of Defects in Thin Film Catalysts via Cluster Analysis
Aileen Luo, Oleg Yu. Gorobtsov, Jocienne N. Nelson +9
Functional properties of transition-metal oxides strongly depend on crystallographic defects. In transition-metal-oxide electrocatalysts such as SrIrO3 (SIO), crystallographic latt…
Real-time X-ray Phase-contrast Imaging Using SPINNet -- A Speckle-based Phase-contrast Imaging Neural Network
Zhi Qiao, Xianbo Shi, Yudong Yao +4
X-ray phase-contrast imaging has become indispensable for visualizing samples with low absorption contrast. In this regard, speckle-based techniques have shown significant advantag…
High-Performance Ptychographic Reconstruction with Federated Facilities
Tekin Bicer, Xiaodong Yu, Daniel J. Ching +5
Beamlines at synchrotron light source facilities are powerful scientific instruments used to image samples and observe phenomena at high spatial and temporal resolutions. Typically…
AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imaging
Yudong Yao, Henry Chan, Subramanian Sankaranarayanan +3
The problem of phase retrieval, or the algorithmic recovery of lost phase information from measured intensity alone, underlies various imaging methods from astronomy to nanoscale i…
Proton distribution visualization in perovskite nickelate devices utilizing nanofocused X-rays
Ivan A. Zaluzhnyy, Peter O. Sprau, Richard Tran +17
We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO-based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-f…
Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials
Suvo Banik, Troy D Loeffler, Rohit Batra +3
Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends…
High resolution functional imaging through Lorentz transmission electron microscopy and differentiable programming
Tao Zhou, Mathew Cherukara, Charudatta Phatak
Lorentz transmission electron microscopy is a unique characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materi…
Real-time 3D Nanoscale Coherent Imaging via Physics-aware Deep Learning
Henry Chan, Youssef S. G. Nashed, Saugat Kandel +4
Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging fr…
Real-time sparse-sampled Ptychographic imaging through deep neural networks
Mathew J. Cherukara, Tao Zhou, Youssef Nashed +4
Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving…
Screening of Therapeutic Agents for COVID-19 using Machine Learning and Ensemble Docking Simulations
Rohit Batra, Henry Chan, Ganesh Kamath +3
The world has witnessed unprecedented human and economic loss from the COVID-19 disease, caused by the novel coronavirus SARS-CoV-2. Extensive research is being conducted across th…
BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit
Henry Chan, Badri Narayanan, Mathew Cherukara +4
The ever-increasing power of supercomputers coupled with highly scalable simulation codes have made molecular dynamics an indispensable tool in applications ranging from predictive…
Active Learning the Coarse-Grained Energy Landscape For Water Clusters From Sparse Training Data
Troy D. Loeffler, Tarak K. Patra, Henry Chan +2
ANNs are currently trained by generating large quantities (On the order of or greater) of structural data in hopes that the ANN has adequately sampled the energy landscape…
A coarse-grained deep neural network model for liquid water
Tarak K Patra, Troy D. Loeffler, Henry Chan +3
We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively ag…