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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

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

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

Leveraging Theory for Enhanced Machine Learning

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
Created March 19, 2020, Updated May 5, 2023