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Austin McDannald (Fed)

Materials Research Engineer

Dr. McDannald is a staff material scientist at NIST. He received his B.S. in Physics from Worcester Polytechnic Institute in 2010 and Ph.D in Materials Science and Engineering from the University of Connecticut in 2016. At NIST he works on developing autonomous experimental material science systems. His particular interest is on encoding physics into the machine learning algorithms used to drive autonomous experiments.

Publications

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

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
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Data and Software Publications

Theory aware Machine Learning (TaML)

Author(s)
Debra J. Audus, Austin McDannald, Brian DeCost
A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the

Data for Intrinsic DAC calculations

Author(s)
Austin McDannald, Daniel Siderius
Results of calculations and simulations for the Intrinsic Direct Air Capture analysis of Metal Organic Framwork (MOF) sorbents.Includes Grand Canonical Monte Carlo (GCMC) simulations, predictions of
Created March 19, 2020, Updated May 5, 2023
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