4 papers
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
Daniel Persaud, Mike Werezak, Mark Xu +9
Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Ma…
Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning
Wonseok Jeong, Francesca Tavazza, Brian DeCost
Atomic-scale modeling has advanced rapidly through integration of machine learning, yet a key bottleneck remains. Even with an accurate potential energy surface and a clear target…
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
Ashley S. Dale, Kangming Li, Brian DeCost +4
Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty…
Intrinsic Direct Air Capture
Austin McDannald, Daniel W. Siderius, Brian DeCost +2
We present new metrics to evaluate solid sorbent materials for Direct Air Capture (DAC). These new metrics provide a theoretical upper bound on CO2 captured per energy as well as a…