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Search Publications by: Aaron Gilad Kusne (Fed)

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Displaying 1 - 25 of 63

Workshop Report on Autonomous Methodologies for Accelerating X-ray Measurements

November 5, 2024
Author(s)
Zachary Trautt, Austin McDannald, Brian DeCost, Howard Joress, A. Gilad Kusne, Francesca Tavazza, Tom Blanton
The National Institute of Standards and Technology and the International Centre for Diffraction Data co-hosted a workshop on 17-18 October 2023 to identify and prioritize the goals, challenges, and opportunities for critical and emerging technology needs

Driving U.S. Innovation in Materials and Manufacturing using AI and Autonomous Labs

August 14, 2024
Author(s)
Howie Joress, Zachary Trautt, Austin McDannald, Brian DeCost, A. Gilad Kusne, Francesca Tavazza
With the goal of advancing US competitiveness and excellence in the materials and manufacturing industries, we present our vision for the National Center for Autonomous Materials Science. The objective of this center is to enable and promote the use of

Human-in-the-loop for Bayesian autonomous materials phase mapping

February 7, 2024
Author(s)
Felix Adams, Austin McDannald, Ichrio Takeuchi, A. Gilad Kusne
Autonomous experimentation achieves user objectives more efficiently than Edisonian studies by combining machine learning and laboratory automation to iteratively select and perform experiments. Integrating knowledge from theory, simulations, literature

AI for Materials

April 25, 2023
Author(s)
Debra Audus, Kamal Choudhary, Brian DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
The application of artificial intelligence (AI) methods to materials re- search and development (MR&D) is poised to radically reshape how materials are discovered, designed, and deployed into manufactured products. Materials underpin modern life, and

Scalable Multi-Agent Lab Framework for Lab Optimization

April 11, 2023
Author(s)
A. Gilad Kusne, Austin McDannald
Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises – how will they work together across large

Privacy policies robustness to reverse engineering

October 21, 2022
Author(s)
A. Gilad Kusne
Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected is exposed online

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

September 22, 2022
Author(s)
Austin McDannald, Howie Joress, Brian DeCost, Avery Baumann, A. Gilad Kusne, Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, Winnie Wong-Ng, Andrew J. Allen, Christopher Stafford, Diana Ortiz-Montalvo
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide ( CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously

Analyzing Data Privacy for Edge Systems

July 14, 2022
Author(s)
Olivera Kotevska, Jordan Johnson, A. Gilad Kusne
Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge

Benchmarking Active Learning Strategies for Materials Optimization and Discovery

July 9, 2022
Author(s)
Alex Wang, Haotong Liang, Austin McDannald, Ichiro Takeuchi, A. Gilad Kusne
Autonomous physical science is revolutionizing materials science. In these systems, machine learning (ML) controls experiment design, execution and analysis in a closed loop. Active learning, the ML field of optimal experiment design, selects each

Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties

July 1, 2022
Author(s)
Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, A. Gilad Kusne, Austin McDannald, Diana Ortiz-Montalvo
The increasing CO$_2$ level is a critical concern and suitable materials are needed to directly capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow

Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping

June 29, 2022
Author(s)
Haotong Liang, Valentin Stanev, A. Gilad Kusne, Yuuto Tsukahara, Ama Itou, Ryota Takahashi, Mikk Lippmaa, Ichiro Takeuchi
We have developed a phase mapping method based on machine learning analysis of reflection high-energy electron diffraction (RHEED) images. RHEED produces diffraction patterns containing a wealth of static and dynamic information and is commonly used to

On-the-Fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning

June 1, 2022
Author(s)
Austin McDannald, Matthias Frontzek, Andrew Savici, Mathieu Doucet, Efrain Rodriguez, Kate Meuse, Jessica Opsahl-Ong, Daniel Samarov, Ichiro Takeuchi, William Ratcliff, A. Gilad Kusne
We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the

A Low-Cost Robot Science Kit for Education

April 8, 2022
Author(s)
Logan Saar, Haotong Liang, Alex Wang, Austin McDannald, Efrain Rodriguez, Ichiro Takeuchi, A. Gilad Kusne
The next generation of physical science involves robot scientists – autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Such systems have shown real-world success for scientific exploration and