5 citations · 7 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2019★ 2 cited
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…
stat.ML2019★ 5 cited
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization
Zachary del Rosario, Matthias Rupp, Yoolhee Kim +2
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error}…
physics.flu-dyn2019
Generalization of machine-learned turbulent heat flux models applied to film cooling flows
Pedro M. Milani, Julia Ling, John K. Eaton
The design of film cooling systems relies heavily on Reynolds-Averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbul…