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Search Publications by: Aaron Gilad Kusne (Fed)

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Displaying 1 - 25 of 31

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

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

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

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

February 16, 2022
Author(s)
A. Gilad Kusne, Austin McDannald, Brian DeCost
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown

A Semi-Supervised Approach for Automatic Crystal Structure Classification

November 1, 2021
Author(s)
Satvik Lolla, Haotong Liang, A. Gilad Kusne, Ichiro Takeuchi, William D. Ratcliff
The structural solution problem can be a daunting and time consuming task. Especially in the presence of impurity phases, current methods such as indexing become more unstable. In this work, we apply the novel approach of semi-supervised learning towards

Artificial intelligence for search and discovery of quantum materials

October 13, 2021
Author(s)
A. Gilad Kusne, Ichiro Takeuchi, Valentin Stanev, Johnpierre Paglione
Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

June 9, 2021
Author(s)
Jason Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howard Joress, Winnie Wong-Ng, Debra Kaiser, Andriy Zakutayev, Caleb Phillips, Tonio Buonassisi, Shijing Sun, Janak Thapa
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts

On-the-fly closed-loop materials discovery via Bayesian active learning

November 24, 2020
Author(s)
Aaron Gilad Kusne, Heshan Yu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Albert Davydov, Leonid A. Bendersky, Apurva Mehta, Ichiro Takeuchi
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

November 12, 2020
Author(s)
Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

July 14, 2020
Author(s)
Brian L. DeCost, Jason R. Hattrick-Simpers, Zachary T. Trautt, Aaron G. Kusne, Martin L. Green, Eva Campo
Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

July 22, 2019
Author(s)
Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in computer sciences, availability and access of software and hardware, and a growing realization

An Inter-Laboratory Comparative High Throughput Experimental Materials Study of Zn-Sn-Ti-O Thin Films

March 19, 2019
Author(s)
Jason R. Hattrick-Simpers, Zachary T. Trautt, Kamal Choudhary, Aaron G. Kusne, Feng Yi, Martin L. Green, Sara Barron, Andriy Zakutayev, Nam Nguyen, Caleb Phillips, John Perkins, Ichiro Takeuchi, Apurva Mehta
High throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many possible applications. However, there are very few publications on the reproducibility of the HTE results

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

August 6, 2018
Author(s)
Valentin Stanev, Velimir Vesselinov, Aaron Gilad Kusne, Graham Antoszewski, Ichiro Takeuchi, Boian Alexandrov
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of

Machine learning modeling of superconducting critical temperature

June 28, 2018
Author(s)
Aaron Gilad Kusne, Valentin Stanev, Ichiro Takeuchi
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical