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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

Intrinsic Direct Air Capture

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
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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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