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Artificial intelligence / Autonomous systems

Autonomous laboratories

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Displaying 1 - 25 of 34

An Autonomous Agent for Soft Material Structural Optimization and Discovery

June 6, 2025
Author(s)
Tyler Martin, Austin McDannald, Aaron Kusne, Peter Beaucage
The pace of formulation (re)development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed an

Materials laboratories of the future for alloys, amorphous, and composite materials

January 29, 2025
Author(s)
Sarbajit Banerjee, Y. S. Meng, Andrew M. Minor, Minghao Zhang, Nestor J. Zaluzec, Maria K. Chan, Gerald Seidler, David W. McComb, Joshua Agar, Partha P. Mukherjee, Brent Melot, Karena Chapman, Beth S. Guiton, Robert F. Klie, Ian D. McCue, Paul M. Voyles, Ian Robertson, Ling Li, Miaofang Chi, Joel F. Destino, Arun Devaraj, Emmanuelle Marquis, Carlo U. Segre, Huinan H. Liu, Judith C. Yang, Kasra Momeni, Amit Misra, Niaz Abdolrahim, Julia E. Medvedeva, Wenjun Cai, Alp Sehirlioglu, Melike Dizbay-Onat, Apurva Mehta, Lori Graham-Brady, Benji Maryuama, Krishna Rajan, Jamie H. Warner, Mitra L. Taheri, Sergei V. Kalinin, B. Reeja-Jayan, Udo D. Schwarz, Sindee L. Simon, Craig Brown
In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a

AutoRefl: Active Learning in Neutron Reflectometry for Fast Data Acquisition

November 30, 2024
Author(s)
David Hoogerheide, Frank N. Heinrich
Neutron reflectometry (NR) is a powerful technique for interrogating the structure of thin films at interfaces. Because NR measurements are slow and instrument availability is limited, measurement efficiency is paramount. One approach to improving

Workshop Report on Autonomous Methodologies for Accelerating X-ray Measurements

November 5, 2024
Author(s)
Zachary Trautt, Austin McDannald, Brian DeCost, Howard Joress, Aaron 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)
Howard Joress, Zachary Trautt, Austin McDannald, Brian DeCost, Aaron 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, Takeuchi Ichiro, Aaron 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

Autonomous cementitious materials formulation for critical infrastructure repair

January 17, 2024
Author(s)
Howard Joress, Rachel Cook, Austin McDannald, Mark Kozdras, Jason hattrick-simpers, Aron Newman, Scott Jones
Despite the long history of cement and concrete, there are still large gaps in fundamental knowledge of their properties development. The dire need for improved cementitious repair materials, requires a revolution in the way they are formulated and tested

A Call for Caution in the Era of AI-Accelerated Materials Science

December 6, 2023
Author(s)
Kangming Li, Edward Kim, Yao Fehlis, Daniel Persaud, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers
It is safe to state that the field of matter has successfully entered the fourth paradigm, where machine learning and artificial intelligence (AI) are universally seen as useful, if not truly intelligent. AI's utilization is near-ubiquitous from the

What is missing in autonomous discovery: Open challenges for the community

October 16, 2023
Author(s)
Philip Maffetone, Howard Joress, Shijing Sun
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social

AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy

August 9, 2023
Author(s)
Runze Zhang, Robert Black, Debashish Sur, Parisa Karimi, Kangming Li, Brian DeCost, John Scully, Jason Hattrick-Simpers
Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-source tool named AutoEIS that assists EIS analysis by automatically proposing

AI for Materials

April 25, 2023
Author(s)
Debra Audus, Kamal Choudhary, Brian DeCost, Aaron Kusne, Francesca Tavazza, James 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)
Aaron 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

Self-driving Multimodal Studies at User Facilities

January 22, 2023
Author(s)
Bruce Ravel, Phillip Michael Maffettone, Daniel Allan, Stuart Campbell, Matthew Carbone, Brian DeCost, Howard Joress, Dmitri Gavrilov, Marcus Hanwell, Joshua Lynch, Stuart Wilkins, Jakub Wlodek, Daniel Olds
Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

September 22, 2022
Author(s)
Austin McDannald, Howard Joress, Brian DeCost, Avery Baumann, Aaron Kusne, Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, Winnie Wong-Ng, Andrew 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

Development of an automated millifluidic platform and data-analysis pipeline for rapid electrochemical corrosion measurements: a pH study on Zn-Ni

July 25, 2022
Author(s)
Howard Joress, Brian DeCost, Najlaa Hassan, Trevor Braun, Justin Gorham, Jason Hattrick-Simpers
We describe the development of a millifluidic based scanning droplet cell platform for rapid and automated corrosion. This system allows for measurement of corrosion properties (e.g., open circuit potential, corrosion current through Tafel and linear

Benchmarking Active Learning Strategies for Materials Optimization and Discovery

July 9, 2022
Author(s)
Alex Wang, Haotong Liang, Austin McDannald, Ichiro Takeuchi, Aaron 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

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, Aaron 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, Aaron 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, Takeuchi Ichiro, Aaron 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)
Aaron 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
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