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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...

Data and Software Publications

Dataset of Additive Manufacturing Laser Powder Bed Fusion Ti-6Al-4V Subjected to Various Novel Hot Isostatic Pressing (HIP) Treatments

Author(s)
Nicholas Derimow, Jake T. Benzing, Newell Moser, Orion L. Kafka, Howie Joress, Austin McDannald, Lucas Koepke, David Newton, Jacob Garcia, Benjamin Caplins, Ping Lu, Frank W. DelRio, Chad Beamer, Ryan Fishel, Nik Hrabe
This publicly available dataset encompasses high pressure heat treatment development for laser powder bed fusion (PBF-L) Ti-6Al-4V. In order to understand microstructure as it influences processing

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
Created March 19, 2020, Updated May 5, 2023
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