2 citations · 6 across the 7 of their papers we have counts for
7 papers · 1 filter
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
Francesca Tavazza, Kamal Choudhary, Brian DeCost
The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used…
Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks
Md Habibur Rahman, Prince Gollapalli, Panayotis Manganaris +5
Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semicon…
Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design
Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando +7
The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in v…
AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy
Runze Zhang, Robert Black, Debashish Sur +5
Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-sourc…
Self-driving Multimodal Studies at User Facilities
Phillip M. Maffettone, Daniel B. Allan, Stuart I. Campbell +11
Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days t…
Reproducible Sorbent Materials Foundry for Carbon Capture at Scale
Austin McDannald, Howie Joress, Brian DeCost +9
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organi…