48 citations · 55 across the 5 of their papers we have counts for
5 papers
A case study of multi-modal, multi-institutional data management for the combinatorial materials science community
Sarah I. Allec, Eric S. Muckley, Nathan S. Johnson +9
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functio…
Interpretable models for extrapolation in scientific machine learning
Eric S. Muckley, James E. Saal, Bryce Meredig +2
Data-driven models are central to scientific discovery. In efforts to achieve state-of-the-art model accuracy, researchers are employing increasingly complex machine learning algor…
By how much can closed-loop frameworks accelerate computational materials discovery?
Lance Kavalsky, Vinay I. Hegde, Eric Muckley +3
The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discove…
Quantifying the performance of machine learning models in materials discovery
Christopher K. H. Borg, Eric S. Muckley, Clara Nyby +4
The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or…
AutoMat: Accelerated Computational Electrochemical systems Discovery
Emil Annevelink, Rachel Kurchin, Eric Muckley +17
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and…