2 citations · 2 across the 1 of their papers we have counts for
3 papers
From Atomistic Models to Machine Learning: Predictive Design of Nanocarbons under Extreme Conditions
Xiaoli Yan, Millicent A. Firestone, Murat Keceli +2
The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but ho…
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
Marcus Schwarting, Logan Ward, Nathaniel Hudson +5
Generative AI poses both opportunities and risks for solving inverse design problems in the sciences. Generative tools provide the ability to expand and refine a search space auton…
MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
Xiaoli Yan, Nathaniel Hudson, Hyun Park +15
We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance com…