5 citations · 10 across the 3 of their papers we have counts for
3 papers
Quantifying uncertainty in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD
Vinay I. Hegde, Christopher K. H. Borg, Zachary del Rosario +7
A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be use…
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…
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}…