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Search Publications by: Austin McDannald (Fed)

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Displaying 1 - 9 of 9

Overcoming Roadblocks for Implementing AI/ML Methods for Materials Advancement

April 6, 2026
Author(s)
James Warren, Francesca Tavazza, Austin McDannald, Aaron Kusne, Howard Joress, David Hoogerheide, Brian DeCost, Kamal Choudhary, Debra Audus
The development of novel materials with tailored properties is a complex, multi-objective optimization problem that has long been a challenge in materials research. The integration of artificial intelligence (AI) and machine learning (ML) techniques has

Intrinsic Direct Air Capture

September 10, 2025
Author(s)
Austin McDannald, Daniel Siderius, Brian DeCost, Kamal Choudhary, Diana Ortiz-Montalvo
How can you tell if a sorbent material will be good for any gas separation process – without having to do detailed simulations of the full process? We present new metrics to evaluate solid sorbent materials for Direct Air Capture (DAC), a particularly

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

Microstructure and mechanical properties of laser powder bed fusion Ti-6Al-4V after HIP treatments with varied temperatures and cooling rates

October 22, 2024
Author(s)
Nicholas Derimow, Jake Benzing, Howard Joress, Austin McDannald, Ping Lu, Frank DelRio, Newell Moser, Matthew Connolly, Alec Saville, Orion Kafka, Chad Beamer, Ryan Fishel, Chris Hadley, Nikolas Hrabe
This work investigated non-standard HIP cycles for PBF-L Ti-6Al-4V and characterized microstructure and tensile properties to compare between material that originated from the same build. For 920 °C, faster cooling rates (100 °C/min, 2000 °C/min) were

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

Leveraging Theory for Enhanced Machine Learning

August 26, 2022
Author(s)
Debra Audus, Austin McDannald, Brian DeCost
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is

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
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