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Brian DeCost (Fed)

Brian DeCost is a materials research engineer in the Data and AI Driven Materials Science Group at the National Institute of Standards and Technology. He earned a B.S. in Chemical Engineering at the University of Florida and a Ph.D. in Materials Science and Engineering at Carnegie Mellon University. Brian’s research focuses on developing and applying scientific machine learning methods and automation tools to address fundamental and applied problems in microstructure science and alloy design, with a particular focus on active learning for autonomous experiment planning and execution.

 

postdoctoral opportunity: Scientific machine learning methods for trustable accelerated materials characterization and design

Publications

Data and Software Publications

ALIGNN: Atomistic Line Graph Neural Network

Author(s)
Kamal Choudhary, Brian DeCost
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models

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 September 12, 2019, Updated April 11, 2024
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