80 citations · 160 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Machine-learned metrics for predicting the likelihood of success in materials discovery
Yoolhee Kim, Edward Kim, Erin Antono +2
Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials…
Robust FCC solute diffusion predictions from ab-initio machine learning methods
Henry Wu, Aren Lorenson, Ben Anderson +4
We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous de…